Journal articles on the topic 'Signal-to-noise-ratio (SNR)'
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Bosworth, B. T., W. R. Bernecky, J. D. Nickila, B. Adal, and G. C. Carter. "Estimating Signal-to-Noise Ratio (SNR)." IEEE Journal of Oceanic Engineering 33, no. 4 (October 2008): 414–18. http://dx.doi.org/10.1109/joe.2008.2001780.
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Kolar, Petar, Lovro Blažok, and Dario Bojanjac. "NMR spectroscopy threshold signal-to-noise ratio." tm - Technisches Messen 88, no. 9 (April 17, 2021): 571–80. http://dx.doi.org/10.1515/teme-2021-0008.
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Abstract Ever since noise was spotted and proven to cause problems for the transmission and detection of information through a communication channel, a standard procedure in the process of characterizing a detection system of the communication channel is to determine the level of the lowest detectable signal. In signal processing, this is usually done by determining the so-called threshold signal-to-noise ratio (SNR). This determination is especially important for the communication channels and systems that constantly operate with low-level signals. A good example of such a system is definitely the NMR spectroscopy system. However, to the authors’ knowledge, the threshold SNR value of NMR spectroscopy systems has not been determined yet. That is why the experts in the field of NMR spectroscopy were asked to assess, using an online questionnaire, which SNR level they considered to be the NMR threshold SNR level. Afterwards, the threshold value was calculated from the obtained data. Finally, it was compared to the existing rule of thumb and thus, a conclusion about its legitimacy was made. The described questionnaire is still available online (https://forms.gle/Y9hyDZ1v1iJoEbk27). This enables everyone to form their own opinion about the threshold SNR level, which the authors encourage the readers to do.
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Baddour, Natalie, and Zuwen Sun. "Photoacoustics Waveform Design for Optimal Signal to Noise Ratio." Symmetry 14, no. 11 (October 24, 2022): 2233. http://dx.doi.org/10.3390/sym14112233.
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Time-frequency analysis in waveform engineering can be applied to many detection and imaging systems, such as radar, sonar, and ultrasound to improve their Signal-to-Noise Ratio (SNR). Recently, photoacoustic imaging systems have attracted researchers’ attention. However, the SNR optimization problem for photoacoustic systems has not been fully addressed. In this paper, the one-dimensional SNR optimization of the photoacoustic response to an input waveform with finite duration and energy was considered. This paper applied an eigenfunction optimization approach to find the waveform for optimal SNR for various photoacoustic absorber profiles. SNR gains via the obtained optimal waveform were compared with simple square-pulse and pulsed sinusoidal waveforms in simulations. Results showed that by using the optimal waveform, SNR can be enhanced especially if the input wave duration is comparable with the absorber time profile duration. The optimal waveforms can achieve 5–10% higher SNR than square pulses and over 100% higher SNR compared with pulsed sinusoids. The symmetry between time and frequency domains assures similar behavior when temporal durations of the input waveforms are too short or too long compared with the absorber.
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Czanner, Gabriela, Sridevi V. Sarma, Demba Ba, Uri T. Eden, Wei Wu, Emad Eskandar, Hubert H. Lim, Simona Temereanca, Wendy A. Suzuki, and Emery N. Brown. "Measuring the signal-to-noise ratio of a neuron." Proceedings of the National Academy of Sciences 112, no. 23 (May 20, 2015): 7141–46. http://dx.doi.org/10.1073/pnas.1505545112.
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The signal-to-noise ratio (SNR), a commonly used measure of fidelity in physical systems, is defined as the ratio of the squared amplitude or variance of a signal relative to the variance of the noise. This definition is not appropriate for neural systems in which spiking activity is more accurately represented as point processes. We show that the SNR estimates a ratio of expected prediction errors and extend the standard definition to one appropriate for single neurons by representing neural spiking activity using point process generalized linear models (PP-GLM). We estimate the prediction errors using the residual deviances from the PP-GLM fits. Because the deviance is an approximate χ2 random variable, we compute a bias-corrected SNR estimate appropriate for single-neuron analysis and use the bootstrap to assess its uncertainty. In the analyses of four systems neuroscience experiments, we show that the SNRs are −10 dB to −3 dB for guinea pig auditory cortex neurons, −18 dB to −7 dB for rat thalamic neurons, −28 dB to −14 dB for monkey hippocampal neurons, and −29 dB to −20 dB for human subthalamic neurons. The new SNR definition makes explicit in the measure commonly used for physical systems the often-quoted observation that single neurons have low SNRs. The neuron’s spiking history is frequently a more informative covariate for predicting spiking propensity than the applied stimulus. Our new SNR definition extends to any GLM system in which the factors modulating the response can be expressed as separate components of a likelihood function.
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Xie, Xiaojuan, Shengliang Peng, and Xi Yang. "Deep Learning-Based Signal-To-Noise Ratio Estimation Using Constellation Diagrams." Mobile Information Systems 2020 (November 6, 2020): 1–9. http://dx.doi.org/10.1155/2020/8840340.
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Signal-to-noise ratio (SNR) estimation is a fundamental task of spectrum management and data transmission. Existing methods for SNR estimation usually suffer from significant estimation errors when SNR is low. This paper proposes a deep learning (DL) based SNR estimation algorithm using constellation diagrams. Since the constellation diagrams exhibit different patterns at different SNRs, the proposed algorithm achieves SNR estimation via constellation diagram recognition, which can be easily handled based on DL. Three DL networks, AlexNet, InceptionV1, and VGG16, are utilized for DL based SNR estimation. Experimental results show that the proposed algorithm always performs well, especially in low SNR scenarios.
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L. M. Hassan, S., N. Sulaiman, S. S. Shariffudin, and T. N. T. Yaakub. "Signal-to-noise Ratio Study on Pipelined Fast Fourier Transform Processor." Bulletin of Electrical Engineering and Informatics 7, no. 2 (June 1, 2018): 230–35. http://dx.doi.org/10.11591/eei.v7i2.1167.
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Fast Fourier transform (FFT) processor is a prevailing tool in converting signal in time domain to frequency domain. This paper provides signal-to-noise ratio (SNR) study on 16-point pipelined FFT processor implemented on field-programable gate array (FPGA). This processor can be used in vast digital signal applications such as wireless sensor network, digital video broadcasting and many more. These applications require accuracy in their data communication part, that is why SNR is an important analysis. SNR is a measure of signal strength relative to noise. The measurement is usually in decibles (dB). Previously, SNR studies have been carried out in software simulation, for example in Matlab. However, in this paper, pipelined FFT and SNR modules are developed in hardware form. SNR module is designed in Modelsim using Verilog code before implemented on FPGA board. The SNR module is connected directly to the output of the pipelined FFT module. Three different pipelined FFT with different architectures were studied. The result shows that SNR for radix-8 and R4SDC FFT architecture design are above 40dB, which represent a very excellent signal. SNR module on the FPGA and the SNR results of different pipelined FFT architecture can be consider as the novelty of this paper.
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Khairunnisa, Khairunnisa, Nurkamilia Nurkamilia, and Zuraidah Zuraidah. "Analisis Signal-To-Noise Ratio Pada Sinyal Audio Dengan Teknik Konvolusi." Jurnal ELTIKOM 2, no. 2 (December 26, 2018): 78–86. http://dx.doi.org/10.31961/eltikom.v2i2.84.
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Di bangku kuliah, derau dan kaitannya dengan kualitas sinyal biasanya dibahas pada mata kuliah pengolahan sinyal. Salah satu metode yang digunakan adalah metode konvolusi. Algoritma yang digunakan cukup kompleks dan tidak mudah cepat dipahami oleh mahasiswa dan ini merupakan tantangan bagi dosen pengajar. Penulis membuat suatu aplikasi yang dapat menampilkan hasil analisis reduksi sinyal audio dengan teknik konvolusi sehingga dapat memberikan pemahaman yang lebih baik kepada mahasiswa sekaligus membuktikan teori yang sudah ada. Langkah-langkah penelitian yang dilakukan adalah menentukan sinyal audio berderau yang akan dianalisis, kemudian menentukan parameter sinyal yang akan digunakan sebagai variable untuk menghitung SNR. Parameter sinyal yang dimaksud adalah amplitudo, frekuensi analog, dan frekuensi pencuplikan. Langkah selanjutnya adalah menjalankan sistem konvolusi terhadap sinyal objek dengan mengubah-ubah variable, menentukan hasil tanggapan konvolusi sesuai dengan karakteristik sinyal yang kita inginkan direduksi deraunya, menghitung SNR sinyal keluaran, dan membandingkannya dengan SNR hasil konvolusi. Untuk pengujian SNR dengan sinyal sinusoida amplitudo 5 V dan frekuensi pencuplikan 8000 cuplikan/detik pada beberapa nilai frekuensi yaitu, 100 Hz, 500 Hz, 1000 Hz dan 2000 Hz. SNR konvolusi diskrit nilainya lebih rendah daripada SNR konvolusi kontinyu. Semakin tinggi frekuensi, nilai SNR semakin turun. Pada frekuensi audio 852 Hz, SNR sinyal diuji pada beberapa nilai frekuensi pencuplikan yaitu, 8520 cup/s, 25560 cup/s, 51120 cup/s dan 153360 cup/s. SNR konvolusi cenderung naik apabila frekuensi pencuplikan (Fp) ditambah. SNR konvolusi diskrit relative masih lebih rendah daripada SNR konvolusi kontinyu. Pada sinyal audio internal PC bekerja pada frekuensi 8192 Hz. SNR diuji pada beberapa nilai Fp. SNR konvolusi kontinyu relatif konstan, sedangkan SNR konvolusi diskrit relatif meningkat apabila nilai Fp ditambah.
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FENG, TIANQUAN. "SIGNAL-TO-NOISE RATIO GAIN VIA CORRELATED NOISE IN AN ENSEMBLE OF NOISY NEURONS." Journal of Biological Systems 28, no. 01 (March 2020): 111–26. http://dx.doi.org/10.1142/s0218339020500059.
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The collective response of an ensemble of leaky integrate-and-fire neurons induced by local correlated noise is investigated theoretically. Based on the linear response theory, we derive the analytic expression of signal-to-noise ratio (SNR). Numerical results show that the amplitude of internal noise can be increased up to an optimal value where the output SNR reaches a maximum value. Interestingly, we find that the correlated noise between the nearest neurons could lead to the obvious SNR gain. We also show that the SNR can reach unity under condition that the correlated noise between the nearest neurons is negative. This nonlinear amplification of SNR gain in an ensemble of noisy neurons can be related to the array stochastic resonance (SR) phenomenon. Furthermore, we also show that the SNR gain can also be optimized by tuning the number of neuron units, frequency and amplitude of the weak periodic signal.
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Choi, Jae-Seung. "Improvement of Signal-to-Noise Ratio for Speech under Noisy Environment." Journal of the Korean Institute of Information and Communication Engineering 17, no. 7 (July 31, 2013): 1571–76. http://dx.doi.org/10.6109/jkiice.2013.17.7.1571.
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Uaratanawong, Valanon, Chalermchon Satirapod, and Toshiaki Tsujii. "Evaluation of multipath mitigation performance using signal-to-noise ratio (SNR) based signal selection methods." Journal of Applied Geodesy 15, no. 1 (January 27, 2021): 75–85. http://dx.doi.org/10.1515/jag-2020-0045.
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AbstractSatellite signal strength sometimes decreases when multipath exists. This effect reduces signal quality and can lead to a large static positioning error, even the survey-grade receivers are used. Three signal selection methods based on signal-to-noise ratio (SNR) measurements were proposed. The first was the conventional method, based on elevation-dependent average SNR, the second used a moving average of SNR fluctuation and the third method used NLOS exclusion based on SNR residual clustering by the K-means algorithm. To evaluate the positioning accuracy improvement, the static 1 Hz single-point positioning (SPP) test was performed in real-time in two different multipath environments using both dual and quad- constellation GNSS receivers. Trimble and CHC receivers were used at each point to examine the effect on each measurement. Results indicated that the three proposed methods mainly reduced multipath error in horizontal direction compared with the normal SPP.
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Goldberg, Richard L., and Stephen W. Smith. "Optimization of Signal-to-Noise Ratio for Multilayer Pzt Transducers." Ultrasonic Imaging 17, no. 2 (April 1995): 95–113. http://dx.doi.org/10.1177/016173469501700202.
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In medical ultrasound imaging, two-dimensional (2-D) array transducers are desirable to implement dynamic focusing and phase aberration correction in two dimensions as well as volumetric imaging. Unfortunately, the small size of a 2-D array element results in a small clamped capacitance and a large electrical impedance near the resonance frequency. This results in poor signal-to-noise ratio (SNR) of the array elements. It has previously been demonstrated that transducers made from multilayer PZT ceramics have lower electrical impedance and greater SNR than comparable single layer elements. A simplified circuit model has been developed to optimize the SNR for multilayer ceramic (MLC) transducers. In this model, an electronic transmitter excites the array element and in the receive mode, the element drives a coaxial cable load terminated by a high impedance preamplifier. The transducer impedance is Zt/N2, where N is the number of piezoelectric layers. Maximum transmit signal is obtained when N = Ntx such that the transducer impedance, Zt/Ntx2, is matched to the source impedance. Maximum receive signal is obtained when N = Nrx such that the transducer impedance, Zt/Nrx2, is matched to the coaxial cable reactance. For maximum pulse-echo signal, the transducer should be designed with N = [Formula: see text], the geometric mean of Ntx and Nrx. Using this optimization technique, a 1.5-D array was designed with 3 layers for maximum pulse-echo SNR. Results of simulations from the simplified circuit analysis were consistent with those of the KLM model. The 3 layer array was fabricated as well as a single layer control array. The measured transmit signal and receive signal agreed with the simulation results.
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Prabawati, Novelsa Chintya, Siti Masrochah, and Sri Mulyati. "Analisis TSE Factor Terhadap Signal to Noise Ratio dan Contrast to Noise Ratio pada Pembobotan T2 Turbo Spin Echo Potongan Axial MRI Brain." Jurnal Imejing Diagnostik (JImeD) 3, no. 2 (July 10, 2015): 271–76. http://dx.doi.org/10.31983/jimed.v3i2.3198.
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Background: TSE factor is parameters that affect Signal to Noise Ratio (SNR) and Contrast to Noise Ratio (CNR). TSE factor for brain MRI examination is a long TSE factor. There are differences when using TSE factor. At the theory, the brain MRI examination is using TSE factor ≥16 while at Siloam Surabaya Hospital was using TSE factor 14. The writer ever seen some noises at brain MRI image therefore the radiographer doing modification of TSE factor. The purpose of this research are to determine the influence of modification in the TSE factor value against SNR and CNR and to define the SNR and CNR optimum from that.Methods: This research is a quantitative study with an experimental approach. This research was done by MRI Philips Achieva 1,5 T with 10 modification TSE factor (8, 10, 12, 14, 16, 18, 20, 22, 24 and 26). SNR and CNR obtained by measurement of ROI in the grey matter, white matter and CSF with the result an average signal and compared with the average standard deviation of the background image. Data was analyzed by linear regression test to know the influence of TSE factor against SNR and CNR and data was analyzed by descriptive test mean rank to obtain the optimum TSE factor value.Result: The result showed that there was the inluence of TSE factor to SNR and CNR at T2W TSE axial brain. There was a significant correlation between TSE factor with all of area SNR and CNR with coefficient correlation of SNR grey matter r=0,591, with coefficient correlation of SNR white matter r=0,604, with coefficient correlation of SNR CSF r=0,687, with coefficient correlation of CNR CSF–grey matter r=0,690, with coefficient correlation of CNR CSF-white matter r=0,658. The significant value of linear regression test is (0,000*) p value (0,05). TSE factor optimum value at T2W TSE axial brain was TSE factor value 10 for SNR with mean rank SNR 45,05 and TSE factor value 8 for CNR with mean rank CNR 35,43.Conclusion: There was the influence of TSE factor to SNR and CNR at T2W TSE axial brain. TSE factor optimum value in brain MRI T2W TSE axial is 10 to SNR and TSE factor 8 to CNR.
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Thangjai, Warisa, and Sa-Aat Niwitpong. "Confidence Intervals for the Signal-to-Noise Ratio and Difference of Signal-to-Noise Ratios of Log-Normal Distributions." Stats 2, no. 1 (February 27, 2019): 164–73. http://dx.doi.org/10.3390/stats2010012.
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In this article, we propose approaches for constructing confidence intervals for the single signal-to-noise ratio (SNR) of a log-normal distribution and the difference in the SNRs of two log-normal distributions. The performances of all of the approaches were compared, in terms of the coverage probability and average length, using Monte Carlo simulations for varying values of the SNRs and sample sizes. The simulation studies demonstrate that the generalized confidence interval (GCI) approach performed well, in terms of coverage probability and average length. As a result, the GCI approach is recommended for the confidence interval estimation for the SNR and the difference in SNRs of two log-normal distributions.
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Gorbunov, Michael, Vladimir Irisov, and Christian Rocken. "The Influence of the Signal-to-Noise Ratio upon Radio Occultation Retrievals." Remote Sensing 14, no. 12 (June 7, 2022): 2742. http://dx.doi.org/10.3390/rs14122742.
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We study the dependence of radio occultation (RO) inversion statistics on the signal-to-noise ratio (SNR). We use observations from four missions: COSMIC, COSMIC-2, METOP-B, and Spire. All data are processed identically using the same software with the same settings for the retrieval of bending angles, which are compared with reference analyses of the National Oceanic and Atmospheric Administration (NOAA) Global Forecast System. We evaluate the bias, the standard deviation, and the penetration characterized by the fraction of events reaching a specific height. In order to compare SNRs from the different RO missions, we use the results of our previous study, which defined two types of SNR. The statically normalized SNR is defined in terms of the most probable value of the noise floor for the specific mission and global navigation satellite system. The dynamically normalized SNR uses the noise floor value for the specific profile. This study is based on the dynamical normalization. We also evaluate the latitudinal distributions of occultations for different missions. We show that the dependence of the retrieval statistics on the SNR is not very strong, and it is mostly defined by the variations of latitudinal distributions for different SNR. For Spire, these variations are the smallest, and here, the bias and standard deviation reach saturated values for a relatively low SNR.
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MAKRA, PETER, ZOLTAN GINGL, and LASZLO B. KISH. "SIGNAL-TO-NOISE RATIO GAIN IN NON-DYNAMICAL AND DYNAMICAL BISTABLE STOCHASTIC RESONATORS." Fluctuation and Noise Letters 02, no. 03 (September 2002): L147—L155. http://dx.doi.org/10.1142/s0219477502000750.
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It has recently been reported that in some systems showing stochastic resonance, the signal-to-noise ratio (SNR) at the output can significantly exceed that at the input; in other words, SNR gain is possible. We took two such systems, the non-dynamical Schmitt trigger and the dynamical double wellpotential, and using numerical and mixed-signal simulation techniques, we examined what SNR gains these systems can provide. In the non-linear response limit, we obtained SNR gains much greater than unity for both systems. In addition to the classical narrow-band SNR definition, we also measured the ratio of the total power of the signal to the power of the noise part, and it showed even better signal improvement. Here we present a brief review of our results, and scrutinise, for both the Schmitt-trigger and the double well potential, the behaviour of the SNR gain by stochastic resonance for different signal amplitudes and duty cycles. We also discuss the mechanism of providing gains greater than unity.
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Siddiqi, Muhammad Hameed, and Yousef Alhwaiti. "Signal-to-Noise Ratio Comparison of Several Filters against Phantom Image." Journal of Healthcare Engineering 2022 (March 26, 2022): 1–11. http://dx.doi.org/10.1155/2022/4724342.
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Image denoising methods are important in order to diminish various kinds of noises, which are presented either capturing the image or distorted during image transmission. Signal-to-noise ratio (SNR) is one of the main barriers which avoids the theoretical observations to be accomplished in practice. In this study, we have utilized various kinds of filtering operators against three various noises, which are the signal-to-noise ratio comparison against the phantom image in spatial and frequency domain. In frequency domain, the average filter is used to smooth the image and frequency domain, and Gaussian low-pass filter is applied with empirically determined cutoff frequency. This work has six major parts such as applying average filter, determining the SNR of region of interest, transforming the image in frequency domain by discrete Fourier transform, obtaining the rectangular Gaussian low-pass filter along with a cutoff frequency, multiplying them, and carrying out the inverse Fourier transform. These steps are repeated accordingly until the resulting image SNR is equal to or greater than the spatial domain SNR. In order to achieve the goal of this study, we have analyzed the proposed approach against some of complex phantom images. The performances of these filters are compared against signal-to-noise ratio.
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GINGL, ZOLTAN, PETER MAKRA, and ROBERT VAJTAI. "HIGH SIGNAL-TO-NOISE RATIO GAIN BY STOCHASTIC RESONANCE IN A DOUBLE WELL." Fluctuation and Noise Letters 01, no. 03 (September 2001): L181—L188. http://dx.doi.org/10.1142/s0219477501000408.
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We demonstrate that signal-to-noise ratio (SNR) can be significantly improved by stochastic resonance in a double well potential. The overdamped dynamical system was studied using mixed signal simulation techniques. The system was driven by wideband Gaussian white noise and a periodic pulse train with variable amplitude and duty cycle. Operating the system in the non-linear response range, we obtained SNR gains much greater than unity. In addition to the classical SNR definition, the ratio of the total power of the signal to the power of the noise part was also measured and it showed better signal improvement.
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Gorbunov, Michael, Vladimir Irisov, and Christian Rocken. "Noise Floor and Signal-to-Noise Ratio of Radio Occultation Observations: A Cross-Mission Statistical Comparison." Remote Sensing 14, no. 3 (February 1, 2022): 691. http://dx.doi.org/10.3390/rs14030691.
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Multiple radio occultation (RO) missions are currently providing observations that are assimilated by the world’s leading numerical weather prediction centers. These RO missions use the same signals originating from the Global Navigation Satellite Systems (GNSS), but they have different satellite designs and sizes with different antennas and receivers. This results in different noise levels for different missions. Although the amplitude data are characterized by the Signal-to-Noise Ratio (SNR), the noise, to which they are normalized, is not the real Noise Floor (NF) of the RO observations. We study the statistical distributions of the SNR and NF for RO missions including COSMIC, COSMIC2, METOP-A, METOP-B, METOP-C, and Spire. We demonstrate that different missions have different NF values and different NF and SNR distributions, sometimes multimodal. We propose to use the most probable NF value as an SNR normalization constant in order to compare the SNR values from different RO missions.
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He, Di, Xin Chen, Ling Pei, Lingge Jiang, and Wenxian Yu. "Improvement of Noise Uncertainty and Signal-To-Noise Ratio Wall in Spectrum Sensing Based on Optimal Stochastic Resonance." Sensors 19, no. 4 (February 18, 2019): 841. http://dx.doi.org/10.3390/s19040841.
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Noise uncertainty and signal-to-noise ratio (SNR) wall are two very serious problems in spectrum sensing of cognitive radio (CR) networks, which restrict the applications of some conventional spectrum sensing methods especially under low SNR circumstances. In this study, an optimal dynamic stochastic resonance (SR) processing method is introduced to improve the SNR of the receiving signal under certain conditions. By using the proposed method, the SNR wall can be enhanced and the sampling complexity can be reduced, accordingly the noise uncertainty of the received signal can also be decreased. Based on the well-studied overdamped bistable SR system, the theoretical analyses and the computer simulations verify the effectiveness of the proposed approach. It can extend the application scenes of the conventional energy detection especially under some serious wireless conditions especially low SNR circumstances such as deep wireless signal fading, signal shadowing and multipath fading.
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He, Zhili, and Jizhong Zhou. "Empirical Evaluation of a New Method for Calculating Signal-to-Noise Ratio for Microarray Data Analysis." Applied and Environmental Microbiology 74, no. 10 (March 14, 2008): 2957–66. http://dx.doi.org/10.1128/aem.02536-07.
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ABSTRACT Signal-to-noise-ratio (SNR) thresholds for microarray data analysis were experimentally determined with an oligonucleotide array that contained perfect-match (PM) and mismatch (MM) probes based upon four genes from Shewanella oneidensis MR-1. A new SNR calculation, called the signal-to-both-standard-deviations ratio (SSDR), was developed and evaluated, along with other two methods, the signal-to-standard-deviation ratio (SSR) and the signal-to-background ratio (SBR). At a low stringency, the thresholds of the SSR, SBR, and SSDR were 2.5, 1.60, and 0.80 with an oligonucleotide and a PCR amplicon as target templates and 2.0, 1.60, and 0.70 with genomic DNAs as target templates. Slightly higher thresholds were obtained under high-stringency conditions. The thresholds of the SSR and SSDR decreased with an increase in the complexity of targets (e.g., target types) and the presence of background DNA and a decrease in the compositions of targets, while the SBR remained unchanged in all situations. The lowest percentage of false positives and false negatives was observed with the SSDR calculation method, suggesting that it may be a better SNR calculation for more accurate determination of SNR thresholds. Positive spots identified by SNR thresholds were verified by the Student t test, and consistent results were observed. This study provides general guidance for users to select appropriate SNR thresholds for different samples under different hybridization conditions.
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Kieser, Robert, Pall Reynisson, and Timothy J. Mulligan. "Definition of signal-to-noise ratio and its critical role in split-beam measurements." ICES Journal of Marine Science 62, no. 1 (January 1, 2005): 123–30. http://dx.doi.org/10.1016/j.icesjms.2004.09.006.
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Abstract The signal-to-noise ratio (SNR) plays a critical role in any measurement but is particularly important in fisheries acoustics where both signal and noise can change by orders of magnitude and may have large variations. “Textbook situations” exist where the SNR is clearly defined, but fisheries-acoustic measurements are generally not in this category as signal and noise come from a wide range of sources that change with location, depth, and ocean conditions. This paper defines the SNR and outlines its measurement using split-beam data. Its effect on target-strength (TS) measurements is explored. Recommendations are given for the routine use of the SNR in fisheries-acoustic measurements. This work also suggests a new equation for TS estimation that is important at low SNR.
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Kumru, Yasin, and Hayrettin Köymen. "Signal-to-noise ratio of diverging waves in multiscattering media: Effects of signal duration and divergence angle." Journal of the Acoustical Society of America 151, no. 2 (February 2022): 955–66. http://dx.doi.org/10.1121/10.0009410.
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In this paper, SNR maximization in coded diverging waves is studied, and experimental verification of the results is presented. Complementary Golay sequences and binary phase shift keying modulation are used to code the transmitted signal. The SNR in speckle and pin targets is maximized with respect to chip signal length. The maximum SNR is obtained in diverging wave transmission when the chip signal is as short a duration as the array permits. We determined the optimum diverging wave profile to confine the transmitted ultrasound energy in the imaging sector. The optimized profile also contributes to the SNR maximization. The SNR performances of the optimized coded diverging wave and conventional single-focused phased array imaging are compared on a single frame basis. The SNR of the optimized coded diverging wave is higher than that of the conventional single-focused phased array imaging at all depths and regions.
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Kumar, Arun Thitai, Jonathan Ophir, and Thomas A. Krouskop. "Noise Performance and Signal-to-Noise Ratio of Shear Strain Elastograms." Ultrasonic Imaging 27, no. 3 (July 2005): 145–65. http://dx.doi.org/10.1177/016173460502700302.
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In this paper, we develop a theoretical expression for the signal-to-noise ratio (SNR) of shear strain elastograms. The previously-developed ideas for the axial strain filter (ASF) and lateral strain filter (LSF) are extended to define the concept of the shear strain filter (SSF). Some of our theoretical results are verified using simulations and phantom experiments. The results indicate that the signal-to-noise ratio of shear-strain elastograms ( SNRsse) improves with increasing shear strain and with improvements in system parameters such as the sonographic signal-to-noise ratio ( SNRs) beamwidth, center frequency and fractional bandwidth. The results also indicate that the amount of axial strain present along with the shear strain is an important parameter that determines the upper bound on SNRsse. The SNRsse will be higher in the absence of additional deformation due to axial strain.
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Mladenov, Valeri, Panagiotis Karampelas, Georgi Tsenov, and Vassiliki Vita. "Approximation Formula for Easy Calculation of Signal-to-Noise Ratio of Sigma-Delta Modulators." ISRN Signal Processing 2011 (February 14, 2011): 1–7. http://dx.doi.org/10.5402/2011/731989.
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The signal-to-noise ratio (SNR) is one of the most significant measures of performance of the sigma-delta modulators. An approximate formula for calculation of signal-to-noise ratio of an arbitrary sigma-delta modulator (SDM) has been proposed. Our approach for signal-to-noise ratio computation does not require modulator modeling and simulation. The proposed formula is compared with SNR calculations based on output bitstream obtained by simulations, and the reasons for small discrepancies are explained. The proposed approach is suitable for fast and precise signal-to-noise ratio computation. It is very useful in the modulator design stage, where multiple performance estimates are required.
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Klingholz, F. "The measurement of the signal-to-noise ratio (SNR) in continuous speech." Speech Communication 6, no. 1 (March 1987): 15–26. http://dx.doi.org/10.1016/0167-6393(87)90066-5.
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Jagadamba, P. "Signal to Noise Ratio(SNR) improvement of atmospheric signals using Variable windows." Signal & Image Processing : An International Journal 3, no. 5 (October 31, 2012): 91–109. http://dx.doi.org/10.5121/sipij.2012.3508.
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Du, Xiao-Yan, Jian-Ying Li, Wen-Jie Zhao, and Jian-Jun Zhang. "Research on Signal to Noise Ratio Characteristic Influence About Temperature Noise to Infrared Compound Eye Lens Array Imaging." Journal of Nanoelectronics and Optoelectronics 17, no. 3 (March 1, 2022): 455–64. http://dx.doi.org/10.1166/jno.2022.3232.
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In order to study the effect of temperature noise on the performance of infrared compound eye lens array imaging system, and based on black body infrared radiation and infrared temperature measurement theory, this paper studied the effects of black body radiation temperature and ambient temperature changing on the imaging performance about signal to noise ratio (SNR) and definition of infrared compound eye lens imaging system. Theoretical mathematical model about influence of temperature noise on infrared compound eye imaging system was built. After finishing numerical simulation analysis and experimental test verification, not only the correctness of the model was proved, but also the conclusion was obtained that the clearer the image of the target presented by the compound eye lens array system, that was, the greater the value of SNR was, under the condition of the lower the ambient temperature noise was relative to the black body radiation temperature noise. Meanwhile, the phenomenon that limited SNR of the imaging capability of infrared compound eye lens array imaging system was about 26.4 db was found in the experiment. A theoretical basis for further research was provided, which was the control method of how to reduce the influence of temperature noise on infrared compound eye lens imaging system and to improve SNR of the system.
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Xu, Pengfei, and Yinjie Jia. "SNR improvement based on piecewise linear interpolation." Journal of Electrical Engineering 72, no. 5 (September 1, 2021): 348–51. http://dx.doi.org/10.2478/jee-2021-0049.
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Abstract Interpolation improves the resolution of the curve. Based on the stationary characteristics of the signal and the non-stationary characteristics of the noise, the theoretical proof indicates that the piecewise linear interpolation can improve the signal-to-noise ratio, which is further confirmed by simulation results.
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Thangjai, Warisa, and Suparat Niwitpong. "Bootstrap Confidence Intervals for Common Signal-to-noise Ratio of Two-parameter Exponential Distributions." Statistics, Optimization & Information Computing 10, no. 3 (May 4, 2022): 858–72. http://dx.doi.org/10.19139/soic-2310-5070-931.
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Signal-to-noise ratio (SNR) is a reciprocal of coefffficient of variation. The SNR is a measure of mean relative to the variability. Confidence procedures for common SNR of two-parameter exponential distributions were developed using generalized confidence interval (GCI) approach, large sample (LS) approach, adjusted method of variance estimates recovery (Adjusted MOVER) approach, and bootstrap approaches based on standard bootstrap (SB) and parametric bootstrap (PB). The performances of all approaches are measured by coverage probability and average length. Simulation studies show that all approaches have the coverage probabilities below the nominal confidence level of 0.95 when the common SNR is negative value. However, the coverage probabilities of all approaches are greater than the nominal confidence level of 0.95 when the common SNR is positive value. Moreover, the LS and AM approaches are the conservative confidence intervals. In addition, the GCI and PB approaches provide the confidence intervals with coverage probabilities close to the nominal confidence level of 0.95 when the sample sizes are large and the common SNR is positive value. The GCI and PB approaches are recommended to estimate the confidence intervals for the common SNR of two-parameter exponential distributions. Finally, all proposed approaches are employed in the data of the survival days of lung cancer patients for a demonstration.
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Journal, Baghdad Science. "Calculations of Signal to Noise Ratio (SNR) for Free Space Optical Communication Systems." Baghdad Science Journal 5, no. 1 (March 2, 2008): 95–100. http://dx.doi.org/10.21123/bsj.5.1.95-100.
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In this paper, we calculate and measure the SNR theoretically and experimental for digital full duplex optical communication systems for different ranges in free space, the system consists of transmitter and receiver in each side. The semiconductor laser (pointer) was used as a carrier wave in free space with the specification is 5mW power and 650nm wavelength. The type of optical detector was used a PIN with area 1mm2 and responsively 0.4A/W for this wavelength. The results show a high quality optical communication system for different range from (300-1300)m with different bit rat (60-140)kbit/sec is achieved with best values of the signal to noise ratio (SNR).
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Oo, Thandar, and Pornchai Phukpattaranont. "Signal-to-Noise Ratio Estimation in Electromyography Signals Contaminated with Electrocardiography Signals." Fluctuation and Noise Letters 19, no. 03 (February 17, 2020): 2050027. http://dx.doi.org/10.1142/s0219477520500273.
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When electromyography (EMG) signals are collected from muscles in the torso, they can be perturbed by the electrocardiography (ECG) signals from heart activity. In this paper, we present a novel signal-to-noise ratio (SNR) estimate for an EMG signal contaminated by an ECG signal. We use six features that are popular in assessing EMG signals, namely skewness, kurtosis, mean average value, waveform length, zero crossing and mean frequency. The features were calculated from the raw EMG signals and the detail coefficients of the discrete stationary wavelet transform. Then, these features are used as inputs to a neural network that outputs the estimate of SNR. While we used simulated EMG signals artificially contaminated with simulated ECG signals as the training data, the testing was done with simulated EMG signals artificially contaminated with real ECG signals. The results showed that the waveform length determined with raw EMG signals was the best feature for estimating SNR. It gave the highest average correlation coefficient of 0.9663. These results suggest that the waveform length could be deployed not only in EMG recognition systems but also in EMG signal quality measurements when the EMG signals are contaminated by ECG interference.
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Zhang, Ying, Hao Wang, Heshen Li, Junhua Sun, Huilan Liu, and Yingshuo Yin. "Optimization Model of Signal-to-Noise Ratio for a Typical Polarization Multispectral Imaging Remote Sensor." Sensors 22, no. 17 (September 1, 2022): 6624. http://dx.doi.org/10.3390/s22176624.
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The signal-to-noise ratio (SNR) is an important performance evaluation index of polarization spectral imaging remote sensors. The SNR-estimation method based on the existing remote sensor is not perfect. To improve the SNR of this model, a partial detector check slant direction is presented in this study, and a polarization extinction ratio related to the internal SNR model of a typical multispectral imaging remote sensor is combined with the vector radiative transfer model to construct the atmosphere 6SV–SNR coupling model. The new result is that the central wavelength of the detection spectrum, the observation zenith angle, and the extinction ratio all affect the SNR of the remote sensor, and the SNR increases with the increase in the central wavelength of the detection spectrum. It is proved that the model can comprehensively estimate the SNR of a typical polarization multispectral imaging remote sensor under different detection conditions, and it provides an important basis for the application evaluation of such remote sensors.
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Li, Ming-Yang, Hang-Fang Zhao, and Chao Sun. "Variation of signal-to-noise ratio of vertical array with sound source depth under wind-generated noise background." Acta Physica Sinica 71, no. 4 (2022): 044302. http://dx.doi.org/10.7498/aps.71.20211654.
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Wind-generated noise is ubiquitous in ocean environments and highly influences the passive sonar performance. Since it originates from sources near the ocean surface, one of its physical features is that it largely represents only the intermediate- and high-order modes. The array-level signal-to-noise ratio (SNR), which includes the array-sampled sound intensity, background noise power, and array gain, is an essential quantity determining the sonar array performance. What is investigated in this work is how the array-level SNR of the vertical line array (VLA) varies with the source depth in downward-refracting shallow water, contributed by the modal structure of the surface noise. On the assumption that the modes are well sampled, it is theoretically demonstrated that the SNR varying with the source depth can be approximated as a linear combination of the lower-order mode-amplitude intensities varying with the water depth. Particularly, when the surface noise especially dominates and the water channel is highly downward refractive, this variation can be represented nearly only by the 1<sup>st</sup>-order mode-amplitude intensity varying with depth. The structure is meaningful in practice. It suggests the SNR will be inherently larger when the source is submerged than it is near the ocean surface, and will be maximized at a source depth slightly below the 1<sup>st</sup>-order mode peak across different source ranges. The above assertions are demonstrated in a typical downward-refracting shallow-water channel; the effects from the dominant degree of the surface noise, sound speed gradient in water column, and array aperture are investigated numerically. The obtained results are shown below. 1) Under certain circumstances, the variation of SNR with source depth is nearly irrelevant to the source range. 2) When the surface noise is more significant, the largest SNR in a certain source range will be more significantly larger than the SNR for the source near the surface, the corresponding source depth will be closer to that presenting the 1<sup>st</sup>-order mode’s peak, and the variation of SNR with source depth is increasingly irrelevant to the source range. 3) A stronger downward-refracting sound speed also enhances this SNR superiority and irrelevance to the source range, but causes the source depth presenting the largest SNR to be more deviated from the 1<sup>st</sup>-order mode’s peak. 4) Although the structure is unraveled on the assumption that the VLA spans the full water column, it can be seen when the VLA does not but covers the low-order modes' main part; when the array aperture is insufficiently large it will become approximately periodic in the source range, with the source depth presenting the largest SNR fluctuating lightly and nearly periodically around the 1<sup>st</sup>-order mode peak.
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Arifah, Ahda Nur, Yeti Kartikasari, and Emi Murniati. "Analisis Perbandingan Nilai Signal to Noise Ratio (SNR) pada Pemeriksaan MRI Ankle Joint dengan Menggunakan Quad Knee Coil dan Flex/Multipurpose Coil." Jurnal Imejing Diagnostik (JImeD) 3, no. 1 (January 9, 2017): 220–24. http://dx.doi.org/10.31983/jimed.v3i1.3188.
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Background : Research on the difference comparison the value of Signal To Noise Ratio (SNR) at MRI Ankle Joint examination using Quad Knee Coil and Flex/Multipurpose Coil at the hospital's radiology installation Telogorejo Semarang. Quad knee coil is a volume coil, is a coil that can act as a transmitter and receiver at the same RF signal (transreceiver). Flex / Multipurpose Coil is a surface coil which has a high SNR for a superficial examination (a small organ). The purpose of this research is to know comparison the value of signal to noise ratio (SNR) and higher the value of signal to noise ratio (SNR) at MRI Ankle Joint examination using Quad Knee Coil and Flex / Multipurpose Coil.Method : This type of research is quantitative experimental approach. The research data which 6 samples. Rate includes images subjectively talocalcaneal interosseous ligament, talocrural joint, subtalar joint, the calcaneus, tibia, talus, and the Achilles tendon. Then the results of the data in Paired T-Test tested.Results : Test results that there are differences in comparison the value of signal to noise ratio (SNR) at MRI Ankle Joint examination using Quad Knee Coil and Flex / Multipurpose Coil which has a p-value / sig for all of 0.002, and each criterion that have talocalcaneal interoseous ligament p value 0.026, talocrural joint p value 0.017, subtalar joint p value 0.001, calcaneus p value 0.002, tibia p value 0.003, talus p value 0.006, and achilles tendon p value 0.012. This is in accordance with the calculated average value SNR on the use of Quad Knee Coil is higher at 110.67 because the coil acts as transreceiver and has two preamplifier so as to improve the SNRConclusion : There is a differences in comparison the value of Signal To Noise Ratio (SNR) at MRI Ankle Joint examination using Quad Knee Coil and Flex / Multipurpose Coil.
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Takada, Kazumasa, Shin-ichi Satoh, and Akiya Kawakami. "Signal-to-Noise Ratio of Brillouin Grating Measurement with Micrometer-Resolution Optical Low Coherence Reflectometry." Sensors 20, no. 3 (February 10, 2020): 936. http://dx.doi.org/10.3390/s20030936.
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Signal-dependent speckle-like noise was the dominant noise in a Brillouin grating measurement with micrometer-resolution optical low coherence reflectometry (OLCR). The noise was produced by the interaction of a Stokes signal with beat noise caused by a leaked pump light via square-law detection. The resultant signal-to-noise ratio (SNR) was calculated and found to be proportional to the square root of the dynamic range (DR) defined by the ratio of the Stokes signal magnitude to the variance of the beat noise. The calculation showed that even when we achieved a DR of 20 dB on a logarithmic scale, the SNR value was only 7 on a linear scale and the detected signal tended to fluctuate over ±14% with respect to the mean level. We achieved an SNR of 24 by attenuating the pump light power entering the balanced mixer by 55 dB, and this success enabled us to measure the Brillouin spectrum distributions of mated fiber connectors and a 3-dB fused fiber coupler with a micrometer resolution as examples of OLCR diagnosis.
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Puspasari, Ratih. "Analisa dan Perancangan Aplikasi Bantu Perbaikan Signal to Noise Ratio (SNR) Dengan Metode Flat-Top Sampling." CCIT Journal 3, no. 2 (January 4, 2010): 220–35. http://dx.doi.org/10.33050/ccit.v3i2.336.
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Noise adalah bunyi yang tidak diinginkan dengan bentuk gelombang yang tidak periodik yang terdapat dikeluaran sistem atau dibagian manapun dalam sistem itu yang biasanya timbul ketika proses perekaman. Noise dapa menyebabkan bunyi atau sinyal suara yang diharapkan menjadi lemah atau bahkan hilang. Timbulnya noise dapat disebabkan dari berbagai sumber. Oleh karena itu diperlukan proses penguatan suara terhadap noise. Untuk mempermudah proses penguatan data suara terhadap tingkat noise, digunakan sistem track record suara pada proses perekaman dengan menggunakan perangkat lunak Cakewalk dan Sonic Foundry. Hasil rekaman suara berdasarkan track-recordnya dianalisis untuk mendapatkan level dan jenis noise. Kemudian, data hasil rekaman suara diperbaiki menggunakan metoda Flat-Top Sampling. Untuk mengetahui penguatan metoda Flat – Top Sampling dalam melakukan pengolahan sinyal, dilakukan perbandingan antara sinyal terhadapa noise yang disebut dengan Signal to Noise Ratio (SNR). SNR keluaran dibandingkan terhadap SNR masukan. Semakin tinggi nilai SNR dari suatu sinyal maka semakin bagus kondisi sinyal tersebut.
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Yang, Kai, Zhitao Huang, Xiang Wang, and Fenghua Wang. "An SNR Estimation Technique Based on Deep Learning." Electronics 8, no. 10 (October 9, 2019): 1139. http://dx.doi.org/10.3390/electronics8101139.
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Signal-to-noise ratio (SNR) is a priori information necessary for many signal processing algorithms or techniques. However, there are many problems exsisting in conventional SNR estimation techniques, such as limited application range of modulation types, narrow effective estimation range of signal-to-noise ratio, and poor ability to accommodate non-zero timing offsets and frequency offsets. In this paper, an SNR estimation technique based on deep learning (DL) is proposed, which is a non-data-aid (NDA) technique. Second and forth moment (M2M4) estimator is used as a benchmark, and experimental results show that the performance and robustness of the proposed method are better, and the applied ranges of modulation types is wider. At the same time, the proposed method is not only applicable to the baseband signal and the incoherent signal, but can also estimate the SNR of the intermediate frequency signal.
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Hasegawa, Hideyuki, and Ryo Nagaoka. "Converting Coherence to Signal-to-noise Ratio for Enhancement of Adaptive Ultrasound Imaging." Ultrasonic Imaging 42, no. 1 (December 5, 2019): 27–40. http://dx.doi.org/10.1177/0161734619889384.
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High-frame-rate ultrasound is an emerging technique for functional ultrasound imaging. However, the lateral spatial resolution and contrast in high-frame-rate ultrasound with an unfocused transmit beam are inherently lower than those in conventional ultrasonic imaging based on the line-by-line acquisition using a focused ultrasonic beam because of the low directivity of the transmit beam. Coherence-based beamforming methods were introduced in ultrasound imaging for improvement of image quality. Such methods improve the lateral spatial resolution using the coherence among ultrasonic echo signals received by individual transducer elements. In this study, a new method based on the signal-to-noise ratio (SNR) among the element echo signals was developed for enhancement of the effect of the coherence factor (CF), which was previously developed for improvement in spatial resolution and contrast. In the proposed method, a new factor, namely, SNR factor, was introduced, and the relationship between the previously developed CF and SNR factor was discussed. The proposed method was implemented in plane wave imaging, and the performance was evaluated by simulated and phantom experiments. In simulation, the lateral spatial resolution and contrast obtained with the conventional CF were 0.23 mm and 47.0 dB, respectively, which were significantly better than 0.39 mm and 15.3 dB obtained by conventional delay-and-sum (DAS) beamforming. Using the proposed method, the lateral spatial resolution and contrast were further improved to 0.12 mm and 69.8 dB, respectively. Similar trends were found also in phantom experiments.
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Kowalewski, Borys, Torsten Dau, and Tobias May. "Perceptual Evaluation of Signal-to-Noise-Ratio-Aware Dynamic Range Compression in Hearing Aids." Trends in Hearing 24 (January 2020): 233121652093053. http://dx.doi.org/10.1177/2331216520930531.
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Dynamic range compression is a compensation strategy commonly used in modern hearing aids. Fast-acting systems respond relatively quickly to the fluctuations in the input level. This allows for more effective compression of the dynamic range of speech and hence enhanced the audibility of its low-intensity components. However, such processing also amplifies the background noise, distorts the modulation spectra of both the speech and the background, and can reduce the output signal-to-noise ratio (SNR). Recently, May et al. proposed a novel SNR-aware compression strategy, in which the compression speed is adapted depending on whether speech is present or absent. Fast-acting compression is applied to speech-dominated time–frequency (T-F) units, while noise-dominated T-F units are processed using slow-acting compression. It has been shown that this strategy provides a similar effective compression of the speech dynamic range as conventional fast-acting compression, while introducing fewer distortions of the modulation spectrum of the background and providing an improved output SNR. In this study, this SNR-aware compression strategy was compared with conventional fast- and slow-acting compression in terms of speech intelligibility and subjective preference in a group of 17 hearing-impaired listeners with varying degree of hearing loss. The results show a speech intelligibility benefit of the SNR-aware compression strategy over the conventional slow-acting system. Furthermore, the SNR-aware approach demonstrates an increased subjective preference compared with both conventional fast- and slow-acting systems.
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Zhang, Beiming, Guoping Chen, and Chun Jiang. "Research on Modulation Recognition Method in Low SNR Based on LSTM." Journal of Physics: Conference Series 2189, no. 1 (February 1, 2022): 012003. http://dx.doi.org/10.1088/1742-6596/2189/1/012003.
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Abstract Modulation mode recognition of radio signal is a committed step between signal detection and signal demodulation. At present, quite a lot studies have fully proved that deep learning algorithms can effectively identify the modulation pattern of radio signals. However, the sudden decline of recognition accuracy under the condition of low signal-to-noise ratio needs to be continuously studied and solved. Inspired by the excellent performance of recurrent neural network in signal recognition, this article optimizes and improves the existing system methods, realizes the noise reduction processing of low signal-to-noise ratio signals, and further solves the problem of low recognition accuracy. Through a large number of experimental tests using RML public dataset, the effectiveness of this paper is verified. The results show that the accuracy of modulation pattern recognition of low signal-to-noise ratio signals reaches an average of 27.2%. At last, the paper analyzes the existing problems and optimization points, and looks forward to the further research of relevant contents in the future.
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Pramana, Kadek Agus Cahya, Ni Putu Rita Jeniyanthi, and I. Bagus Gede Dharmawan. "PENGARUH PENGGUNAAN PARAMETER NUMBER SCAN AVERAGE TERHADAP SIGNAL TO NOISE RATIO DAN SCAN TIME PADA PEMERIKSAAN MAGNETIC RESONANCE IMAGING: STUDI LITERATURE REVIEW." JRI (Jurnal Radiografer Indonesia) 5, no. 1 (May 29, 2022): 48–53. http://dx.doi.org/10.55451/jri.v5i1.108.
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Background : Signal to Noise Ratio (SNR) is a comparison of the magnitude of the signal amplitude and the magnitude of the amplitude of noise an MRI image that can be used to measure the quality of an MRI image. SNR can be increased by increasing the value of the number scan average (NSA). By increasing the NSA, the SNR will also increase, the scan time will be longer and cause motion artifacts. The purpose of this study was to determine the effect of using the parameter Number Scan Average on the Signal to Noise Ratio and Scan Time on examinations using Magnetic Resonance Imaging modalities.
Methods: This study uses a descriptive qualitative method with a study approach literature review regarding the effect of using the parameter Number Scan Average on the Signal to Noise Ratio and Scan Time in the examination using Magnetic Resonance Imaging modalities.
Results: The use of variations in NSA values has an effect on SNR and scan time. Giving high value of the NSA will increase the value of the SNR in the image but, the scan time will be longer which affects the quality of the resulting image.
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Pramana, Kadek Agus Cahya, Ni Putu Rita Jeniyanthi, and I. Bagus Gede Dharmawan. "PENGARUH PENGGUNAAN PARAMETER NUMBER SCAN AVERAGE TERHADAP SIGNAL TO NOISE RATIO DAN SCAN TIME PADA PEMERIKSAAN MAGNETIC RESONANCE IMAGING: STUDI LITERATURE REVIEW." JRI (Jurnal Radiografer Indonesia) 5, no. 1 (May 29, 2022): 48–53. http://dx.doi.org/10.55451/jri.v5i1.108.
Full textAbstract:
Background : Signal to Noise Ratio (SNR) is a comparison of the magnitude of the signal amplitude and the magnitude of the amplitude of noise an MRI image that can be used to measure the quality of an MRI image. SNR can be increased by increasing the value of the number scan average (NSA). By increasing the NSA, the SNR will also increase, the scan time will be longer and cause motion artifacts. The purpose of this study was to determine the effect of using the parameter Number Scan Average on the Signal to Noise Ratio and Scan Time on examinations using Magnetic Resonance Imaging modalities.
Methods: This study uses a descriptive qualitative method with a study approach literature review regarding the effect of using the parameter Number Scan Average on the Signal to Noise Ratio and Scan Time in the examination using Magnetic Resonance Imaging modalities.
Results: The use of variations in NSA values has an effect on SNR and scan time. Giving high value of the NSA will increase the value of the SNR in the image but, the scan time will be longer which affects the quality of the resulting image.
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Zhong, Zhirong, Hongfu Zuo, and Heng Jiang. "A nonlinear total variation based denoising method for electrostatic signal of low signal-to-noise ratio." Advances in Mechanical Engineering 14, no. 11 (November 2022): 168781322211369. http://dx.doi.org/10.1177/16878132221136942.
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Aero-engine electrostatic monitoring technology (EMT) is a novel and effective condition monitoring technology. With the help of EMT, effective monitoring of early failures can be achieved. Since the electrostatic monitoring of the running engine will be strongly interfered, the sampled electrostatic signal has various noise components and low signal-to-noise ratio (SNR). After analyzing the source of the noise components carried by the electrostatic signal, this paper proposes a method for electrostatic signal denoising in a strong interference environment, which is based on the nonlinear total variation theory. In the experiments, the simulated electrostatic measurement signal and the actual test-run electrostatic measurement signal were used as the analysis objects, and the denoising test was carried out by using the proposed method. Meanwhile, the denoising effect was compared and analyzed with other classical methods. The experimental results show that the proposed denoising method can effectively remove random noise, electromagnetic pulse and periodic noise in electrostatic signal, and is more applicable to the measured electrostatic signal with low SNR than the classical electrostatic signal denoising methods such as wavelet threshold denoising method and empirical mode decomposition method.
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Juusola, M., E. Kouvalainen, M. Järvilehto, and M. Weckström. "Contrast gain, signal-to-noise ratio, and linearity in light-adapted blowfly photoreceptors." Journal of General Physiology 104, no. 3 (September 1, 1994): 593–621. http://dx.doi.org/10.1085/jgp.104.3.593.
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Response properties of short-type (R1-6) photoreceptors of the blowfly (Calliphora vicina) were investigated with intracellular recordings using repeated sequences of pseudorandomly modulated light contrast stimuli at adapting backgrounds covering 5 log intensity units. The resulting voltage responses were used to determine the effects of adaptational regulation on signal-to-noise ratios (SNR), signal induced noise, contrast gain, linearity and the dead time in phototransduction. In light adaptation the SNR of the photoreceptors improved more than 100-fold due to (a) increased photoreceptor voltage responses to a contrast stimulus and (b) reduction of voltage noise at high intensity backgrounds. In the frequency domain the SNR was attenuated in low frequencies with an increase in the middle and high frequency ranges. A pseudorandom contrast stimulus by itself did not produce any additional noise. The contrast gain of the photoreceptor frequency responses increased with mean illumination and the gain was best fitted with a model consisting of two second order and one double pole of first order. The coherence function (a normalized measure of linearity and SNR) of the frequency responses demonstrated that the photoreceptors responded linearly (from 1 to 150 Hz) to the contrast stimuli even under fairly dim conditions. The theoretically derived and the recorded phase functions were used to calculate phototransduction dead time, which decreased in light adaptation from approximately 5-2.5 ms. This analysis suggests that the ability of fly photoreceptors to maintain linear performance under dynamic stimulation conditions results from the high early gain followed by delayed compressive feed-back mechanisms.
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Tomchik, Seth M., and Zhongmin Lu. "Modulation of Auditory Signal-to-Noise Ratios by Efferent Stimulation." Journal of Neurophysiology 95, no. 6 (June 2006): 3562–70. http://dx.doi.org/10.1152/jn.00063.2006.
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One of the primary challenges that sensory systems face is extracting relevant information from background noise. In the auditory system, the ear receives efferent feedback, which may help it extract signals from noise. Here we directly test the hypothesis that efferent activity increases the signal-to-noise ratio (SNR) of the ear, using the relatively simple teleost ear. Tone-evoked saccular potentials were recorded before and after efferent stimulation, and the SNR of the responses was calculated. In quiet conditions, efferent stimulation suppressed saccular responses to a tone, reducing the SNR. However, when masking noise was added, efferent stimulation increased the SNR of the saccular responses within a range of stimulus combinations. These data demonstrate that auditory efferent feedback can increase SNR in conditions where a signal is masked by noise, thereby enhancing the encoding of signals in noise. Efferent feedback thus performs a fundamental signal processing function, helping the animal to hear sounds in difficult listening conditions.
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Cheng, Xiao Feng, Feng Guo, Xiao Dong Yuan, and Shao Bo He. "Role of Time Delay and Noise Color in Bistable System with Dichotomous Noise." Advanced Materials Research 295-297 (July 2011): 2147–50. http://dx.doi.org/10.4028/www.scientific.net/amr.295-297.2147.
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The effect of time delay and noise color in a time-delayed bistable system subject to asymmetric dichotomous noise and colored noise as well as to square-wave signal is studied. Applying small delay-time approximation, under the adiabatic limit condition, we obtain the expression of the signal-to-noise ratio (SNR) of the system. By virtue of the SNR expression, we find that, the SNR varies non-monotonously with the delayed-time and the correlation of the colored noise. Moreover, the SNR exhibits SR behavior as a function of the strength of the colored noise
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Emery, Charles D., and Stephen W. Smith. "Improved Signal-to-Noise Ratio in Hybrid 2-D Arrays: Experimental Confirmation." Ultrasonic Imaging 19, no. 2 (April 1997): 93–111. http://dx.doi.org/10.1177/016173469701900201.
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2-D array transducers have shown significant promise for medical ultrasound over conventional linear arrays, at the cost of increasing the number of channels, difficulty of fabrication and array element impedance. The increase in element impedance reduces the power coupled to a 2-D array element from a conventional 50 Ω source in transmit mode. If the array is sparse, which is typical of 2-D arrays, then the net power coupled into the front acoustic load is reduced when compared to a fully sampled aperture. Furthermore, the received signal-to-noise ratio (SNR), when measured through a nonideal amplifier, is degraded because the high impedance 2-D array transducer element cannot efficiently drive the coaxial cable. The reduction in transmit sensitivity and received SNR can be circumvented with the application of multilayer piezoelectric elements. The improvement in transmit occurs because the transducer impedance is better matched to the impedance of the source. In receive, multilayer elements allow more of the open circuit received voltage to fall across the input of the high impedance preamplifier. In this case, the same number of layers are used in transmit and receive. Recently, it has been suggested that separate optimization of the transmit channel and receive channel (a hybrid array) would further improve the pulse-echo SNR. In this paper, we fabricated and tested a hybrid array operating at 1 MHz using a multilayer transmit element and single layer receive element. A 7 Ω transmitter and high impedance preamplifier were placed adjacent to the transmit and receive elements within the transducer assembly. The hybrid pulse-echo SNR improved by 26.4 dB over the conventional array. The experimental result showed good agreement with the KLM model. Furthermore, KLM simulations showed that as the operating frequency of the array increases, the overall improvement over the conventional array increases. For example, a 1.5-D array operating at 2 MHz had an improvement of 30 dB whereas a 7.5 MHz 1.5-D array showed an increase of approximately 38 dB. The separate optimization of the transmit and receive channel for 2-D arrays showed even greater improvement than for 1.5-D arrays. For example, a 2 MHz 2-D array had an improvement of over 44 dB.
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Hamlington, B. D., R. R. Leben, R. S. Nerem, and K. Y. Kim. "The Effect of Signal-to-Noise Ratio on the Study of Sea Level Trends." Journal of Climate 24, no. 5 (March 1, 2011): 1396–408. http://dx.doi.org/10.1175/2010jcli3531.1.
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Abstract Extracting secular sea level trends from the background ocean variability is limited by how well one can correct for the time-varying and oscillating signals in the record. Many geophysical processes contribute time-dependent signals to the data, making the sea level trend difficult to detect. In this paper, cyclostationary empirical orthogonal functions (CSEOFs) are used to quantify and improve the signal-to-noise ratio (SNR) between the secular trend and the background variability, obscuring this trend in the altimetric sea level record by identifying and removing signals that are physically interpretable. Over the 16-yr altimetric record the SNR arising from the traditional least squares method for estimating trends can be improved from 4.0% of the ocean having an SNR greater than one to 9.9% when using a more sophisticated statistical method based on CSEOFs. From a standpoint of signal detection, this implies that the secular trend in a greater portion of the ocean can be estimated with a higher degree of confidence. Furthermore, the CSEOF method improves the standard error on the least squares estimates of the secular trend in 97% of the ocean. The convergence of the SNR as the record length is increased is used to estimate the SNR of sea level trends in the near future as more measurements become available from near-global altimetric sampling.
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Wagaye, Gebremedhn Wubet. "Performance Investigation of Channel Noise Effect in Data Transmission Medium Using Signal to Noise Ratio (SNR)." Indonesian Journal of Electrical Engineering and Computer Science 11, no. 2 (August 1, 2018): 419. http://dx.doi.org/10.11591/ijeecs.v11.i2.pp419-423.
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<p>The noise introduced in the channel obviously affects the bit error rate of the communication system and this has direct impact in the security. Here the main problem is that the receiver terminal decoding techniques can lead to wrong interpretation even if the Bit Error Rate (BER) is acceptable. So the main idea here is to introduce high values of Signal to Noise Ratio (SNR) that can improve the bit error rate which exists due to the noise introduced in the wireless channel.</p>
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Zhang, Li, and Xiu Hua Yuan. "Stochastic Resonance in a Single-Mode Laser System with an Input Pulse Signal." Key Engineering Materials 552 (May 2013): 377–83. http://dx.doi.org/10.4028/www.scientific.net/kem.552.377.
Full textAbstract:
In this paper, we investigated the stochastic resonance (SR) phenomenon in a laser system with correlated pump noise and quantum noise. The signal-to-noise ratio (SNR) is calculated when a square sine pulse signal is added to the system. The effects of the duty cycle of pulse signal and the correlation strength of noises on the SNR are discussed. Some valuable phenomena are investigated to improve the output SNR of laser.