Relevant bibliographies by topics / Signal-to-noise-ratio (SNR)

Academic literature on the topic 'Signal-to-noise-ratio (SNR)'

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Published: 4 June 2021
Last updated: 30 January 2023

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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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Dissertations / Theses on the topic "Signal-to-noise-ratio (SNR)"

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Crotty, Maureen. "Signal to Noise Ratio Effects on Aperture Synthesis for Digital Holographic Ladar." University of Dayton / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1355245759.

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Sciré, Scappuzzo Francesca. "Phase multipath estimation for global positioning system (GPS) using signal-to-noise-ratio (SNR) data." Thesis, Massachusetts Institute of Technology, 1997. http://hdl.handle.net/1721.1/45487.

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Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Earth, Atmospheric, and Planetary Sciences, 1997.
Includes bibliographical references (p. 83-87).
by Francesca Scrié Scappuzzo.
M.S.
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Bhattad, Kapil. "Joint source channel coding for non-ergodic channels: the distortion signal-to-noise ratio (SNR) exponent perspective." Texas A&M University, 2008. http://hdl.handle.net/1969.1/85928.

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We study the problem of communicating a discrete time analog source over a channel such that the resulting distortion is minimized. For ergodic channels, Shannon showed that separate source and channel coding is optimal. In this work we study this problem for non-ergodic channels. Although not much can be said about the general problem of transmitting any analog sources over any non-ergodic channels with any distortion metric, for many practical problems like video broadcast and voice transmission, we can gain insights by studying the transmission of a Gaussian source over a wireless channel with mean square error as the distortion measure. Motivated by different applications, we consider three different non-ergodic channel models - (1) Additive white Gaussian noise (AWGN) channel whose signal-to-noise ratio (SNR) is unknown at the transmitter; (2) Rayleigh fading multiple-input multiple-output MIMO channel whose SNR is known at the transmitter; and (3) Rayleigh fading MIMO channel whose SNR is unknown at the transmitter. The traditional approach to study these problems has been to fix certain SNRs of interest and study the corresponding achievable distortion regions. However, the problems formulated this way have not been solved even for simple setups like 2 SNRs for the AWGN channel. We are interested in performance over a wide range of SNR and hence we use the distortion SNR exponent metric to study this problem. Distortion SNR exponent is defined as the rate of decay of distortion with SNR in the high SNR limit. We study several layered transmissions schemes where the source is first compressed in layers and then the layers are transmitted using channel codes that provide variable error protection. Results show that in several cases such layered transmission schemes are optimal in terms of the distortion SNR exponent. Specifically, if the band- width expansion (number of channel uses per source sample) is b, we show that the optimal distortion SNR exponent for the AWGN channel is b and it is achievable using a superposition based layered scheme. For the L-block Rayleigh fading M x N MIMO channel the optimal exponent is characterized for b < (|N - M|+1)= min(M;N) and b > MNL2. This corresponds to the entire range of b when min(M;N) = 1 and L = 1. The results also show that the exponents obtained using layered schemes which are a small subclass of joint source channel coding (JSCC) schemes are, surprisingly, as good as and better in some cases than achievable exponent of all other JSCC schemes reported so far.
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Gallupe, Gary. "FM, PM and NPR Calculations." International Foundation for Telemetering, 1993. http://hdl.handle.net/10150/608853.

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International Telemetering Conference Proceedings / October 25-28, 1993 / Riviera Hotel and Convention Center, Las Vegas, Nevada
System performance can be ascertained via a number of parameters; one of which is Signal-to-Noise ratio (SNR). SNR is the ratio of the value of the signal to the value of the noise. It is generally expressed in decibels and usually a function of the system bandwidth. Another measure of performance is the Noise-Power ratio (NPR). NPR is the ratio of the noise level within a specific measurement channel when noise is applied to all channels, to the level that is measured within the specific channel with noise applied to all of the channels but not the specific channel.
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Coy, Kelly (Kelly Bishop). "A Comparison of Measures of Signal-To-Noise Ratio, Jitter, Shimmer, and Speaking Fundamental Frequency in Smoking and Nonsmoking Females." Thesis, University of North Texas, 1991. https://digital.library.unt.edu/ark:/67531/metadc500903/.

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Fifteen nonsmoking and fifteen smoking females 19 to 36 years of age were evaluated on measures of signal-to-noise ratio (SNR), jitter, shimmer, and speaking fundamental frequency (F0). The results indicated that: 1) there is a significant difference between female smokers and nonsmokers on measures of SNR, mean, and maximum F0 and, 2) there is no significant difference between female smokers and nonsmokers on measures of jitter, shimmer and minimum F0 . The SNR was found to be a powerful tool which is capable of distinguishing subtle vocal characteristics between the subject groups. It would appear that cigarette smoking may have an impact on the voice before distinct laryngeal pathologies are present.
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Paluri, Suraj, and Sandeep Patluri. "A Study of Impulse Response System Identification." Thesis, Karlstad University, Faculty of Technology and Science, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:kau:diva-1069.

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In system identification, different methods are often classified as parametric or non-parametric methods. For parametric methods, a parametric model of a system is considered and the model parameters are estimated. For non-parametric methods, no parametric model is used and the result of the identification is given as a curve or a function.

One of the non-parametric methods is the impulse response analysis. This approach is dynamic simulation. This thesis introduces a new paradigm for dynamic simulation, called impulse-based simulation. This approach is based on choosing a Dirac function as input, and as a result, the output will be equal to the impulse response. However, a Dirac function cannot be realized in practice, and an approximation has to be used. As a consequence, the output will deviate from the impulse response. Once the impulse response is estimated, a parametric model can be fitted to the estimation.

This thesis aims to determine the parameters in a parametric model from an estimated impulse response. The process of investigating the models is a critical aspect of the project. Correlation analysis is used to obtain the weighting function from the estimates of covariance functions.

Later, a relation formed between the parameters and the estimates (obtained by correlation analysis) in the form of a linear system of equations. Furthermore, simulations are carried out using Monte Carlo for investigating the properties of the two step approach, which involves in correlation analysis to find h-parameters and least squares and total least squares methods to solve for the parameters of the model. In order to evaluate the complete capability of the approach to the noise variation a study of signal to noise ratio and mean, mean square error and variances of the estimated parameters is carried out.

The results of the Monte Carlo study indicate that two-step approach can give rather accurate parameter estimates. In addition, the least squares and total least squares methods give similar results.

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Stenström, Mats. "Computerised Microtomography : Non-invasive imaging and analysis of biological samples, with special reference to monitoring development of osteoporosis in small animals." Doctoral thesis, Linköpings universitet, Medicinsk radiofysik, 2001. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-5030.

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The use of Computerised microtomography (CμT) in biomedical research is well established, with most applications developed at synchrotron facilities. The possibility to non-invasively monitor morphological changes in biological samples, makes it an attractive technique in biomedicine. However, high absorbed doses and long examination times are a disadvantage that limits the possibilities of performing longitudinal examinations. The aim of this work was to optimise CmT using conventional X-ray tubes for applications in non-destructive material testing and for skeleton research in small animals (rat). A calculational model of the imaging system was developed and used to optimise the relation between image quality, expressed as the signal-to-noise ratio (SNR) in detecting a contrasting detail, and imaging time in material testing. The model was modified to optimise the relation between the SNR in detecting a trabecular detail in cancelleous bone and the mean absorbed dose in spongiosa and skin for (rat) tibia and femur. Gastrectomized Sprague-Dawley rats were used to initiate osteoporotic changes. In order to detect differences in between gastrectomized rats and controls, spatial resolutions of 150 mm or better were needed. The minimum absorbed doses in femur spongiosa at SNR = 5 were 1mGy - 700 mGy at spatial resolutions from 100 mm to10 mm. In femur skin, the corresponding minimum absorbed doses were 2 mGy - 2000 mGy. Corresponding values for tibia were 0.3 mGy - 300 mGy for both spongiosa and skin (spatial resolution of 100 mm to10 mm). Taking 0.5 Gy as the tolerance limit for the spongiosa dose, longitudinal studies with six repeated examinations will be possible at a spatial resolution of 25 mm in femur and 17 examinations in tibia.
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Kritzinger, Mieke. "Cortical Auditory Evoked Potential (CAEP) and the chirp Auditory Steady State Response (ASSR) in predicting behavioural hearing thresholds in adults with sensorineural hearing loss." Diss., University of Pretoria, 2019. http://hdl.handle.net/2263/73231.

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Abstract Purpose: To compare the frequency specific Cortical Auditory Evoked Potential (CAEP) and the chirp-evoked 40 Hz Auditory Steady State Response (ASSR) with equivalent residual noise levels for behavioural threshold prediction in adults with normal hearing and with SNHL. Method: The study tested 23 adults with normal hearing and 20 adults with SNHL. The participants were aged between 18–65 years. A repeated measures within- participant descriptive design was used to collect the quantitative data. The participants underwent behavioural pure tone, CAEP and ASSR testing on the same day. Results: Similar CAEP difference scores across frequencies for the participants with normal hearing (mean=12.32-14.40 dB) and with SNHL (mean=10.00-16.47 dB) were measured. However, for the ASSR difference scores across frequencies slightly smaller difference scores were measured for the participants with SNHL (mean=10.17-17.30 dB) than for the participants with normal hearing (mean=11.74- 17.14 dB). CAEP thresholds were significantly closer to the behavioural pure tone thresholds at 500 (p=0.028; mean absolute difference 14.40 dB) and 2000 (p=0.016; mean absolute difference 12.56 dB) Hz for participants with normal hearing. In participants with sensorineural hearing loss, CAEP and ASSR thresholds were measured at similar sensation levels and were not statistically different (p>0.05). Conclusion: For the purpose of threshold estimation, representing the auditory function to the level of the auditory cortex the CAEP was closer to the behavioural hearing thresholds than the 40 Hz ASSR at all frequencies except at 4000 Hz, regardless of the hearing sensitivity. Keywords: Auditory steady state response (ASSR), Cortical auditory evoked potential (CAEP), Auditory evoked potential (AEP), Residual noise, Signal to noise ratio (SNR), objective threshold estimation, awake adults.
Dissertation (MA (Audiology))--University of Pretoria, 2019.
Speech-Language Pathology and Audiology
MA Audiology
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Asif, Rameez. "Design and Implementation of System Components for Radio Frequency Based Asset Tracking Devices to Enhance Location Based Services. Study of angle of arrival techniques, effects of mutual coupling, design of an angle of arrival algorithm, design of a novel miniature reconfigurable antenna optimised for wireless communication systems." Thesis, University of Bradford, 2017. http://hdl.handle.net/10454/17143.

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The angle of arrival estimation of multiple sources plays a vital role in the field of array signal processing as MIMO systems can be employed at both the transmitter and the receiver end and the system capacity, reliability and throughput can be significantly increased by using array signal processing. Almost all applications require accurate direction of arrival (DOA) estimation to localize the sources of the signals. Another important parameter of localization systems is the array geometry and sensor design which can be application specific and is used to estimate the DOA. In this work, various array geometries and arrival estimation algorithms are studied and then a new scheme for multiple source estimation is proposed and evaluated based on the performance of subspace and non-subspace decomposition methods. The proposed scheme has shown to outperform the conventional Multiple Signal Classification (MUSIC) estimation and Bartlett estimation techniques. The new scheme has a better performance advantage at low and high signal to noise ratio values (SNRs). The research work also studies different array geometries for both single and multiple incident sources and proposes a geometry which is cost effective and efficient for 3, 4, and 5 antenna array elements. This research also considers the shape of the ground plane and its effects on the angle of arrival estimation and in addition it shows how the mutual couplings between the elements effect the overall estimation and how this error can be minimised by using a decoupling matrix. At the end, a novel miniaturised multi element reconfigurable antenna to represent the receiver base station is designed and tested. The antenna radiation patterns in the azimuth angle are almost omni-directional with linear polarisation. The antenna geometry is uniplanar printed logspiral with striplines feeding network and biased components to improve the impedance bandwidth. The antenna provides the benefit of small size, and re-configurability and is very well suited for the asset tracking applications.
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Podloucká, Lenka. "Identifikace pauz v rušeném řečovém signálu." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2008. http://www.nusl.cz/ntk/nusl-217266.

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This diploma thesis deals with pause identification with degraded speech signal. The speech characteristics and the conception of speech signal processing are described here. The work aim was to create the reliable recognizing method to establish speech and non-speech segments of speech signal with and without degraded speech signal. The five empty pause detectors were realized in computing environment MATLAB. There was the energetic detector in time domain, two-step detector in spectral domain, one-step integral detector, two-step integral detector and differential detector in cepstrum. The spectral detector makes use of energetic characteristics of speech signal in first step and statistic analysis in second step. Cepstral detectors make use of integral or differential algorithms. The detectors robustness was tested for different types of speech degradation and different values of Signal to Noise Ratio. The test of influence different speech degradation was conducted to compare non-speech detection for detectors by ROC (Receiver Operating Characteristic) Curves.
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Book chapters on the topic "Signal-to-noise-ratio (SNR)"

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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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Thomas, J. A., K. Chakrabarti, R. V. Kaczmarek, and A. Romanyukna. "Automated Method for Measuring Contrast-to-Noise- Ratio (CNR) and Signal-to-Noise-Ratio (SNR) on a Full Field Digital Mammography System (FFDMS)." In Digital Mammography, 128–32. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-642-59327-7_29.

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"Signal-to-Noise Ratio (SNR)." In Encyclopedia of Ocean Engineering, 1706. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-10-6946-8_300721.

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Collins, Christopher M. "Fundamentals of Signal-to-Noise Ratio (SNR)." In Electromagnetics in Magnetic Resonance Imaging Physical Principles, Related Applications, and Ongoing Developments. IOP Publishing, 2016. http://dx.doi.org/10.1088/978-1-6817-4083-6ch2.

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"10 Imaging Basics: Signal-to-Noise Ratio (SNR)." In The Physics of Clinical MR Taught Through Images, edited by Val M. Runge, Wolfgang R. Nitz, and Stuart H. Schmeets. Stuttgart: Georg Thieme Verlag, 2009. http://dx.doi.org/10.1055/b-0034-63908.

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Ding, Xue, Gongbin Qian, and Chunlong He. "Performance Analysis of Aerial Base Station Cellular Network." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2022. http://dx.doi.org/10.3233/faia220512.

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In this paper, we model the cellular network consisting of aerial base stations (ABSs) which follow homogeneous Poisson Point Process (PPP) and an independent collection of random users is considered. We assume that each user communicates with the closest ABS. In particular, we deal the interference signals with Laplace transform. As a result, we can obtain a tractable expression of the coverage probability. We have found that there is a maximum coverage probability which can be reached by an optimal ABS density and with low signal-to-noise ratio (SNR), the optimal ABS density gets larger when path loss exponent (PLE) increases. Simulations under high or low SNR condition have shown that the influence of PLE on coverage probability is quite different in spite of changing signal-to-interference-plus-noise ratio (SINR) threshold or ABS density.
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Martinez R., Alejandro D., and On behalf of DarkSide Collaboration. "Real Time Digital Filter for a Front-End Electronics in Dark Matter and Neutrino Measurements." In Machine Learning and Artificial Intelligence. IOS Press, 2020. http://dx.doi.org/10.3233/faia200791.

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This paper presents real-time digital filter algorithms to be applied within dark matter and neutrino measurements. The digital signal processing algorithm implements a trapezoidal pulse-shaper programmed on FPGA at 125 MHz. The real-time filter algorithm enhances the SNR of a digitized signal from a photo detection module (SiPM, cryogenic front-end electronics & 14-bits ADC). The trapezoidal filter upgrades the signal to noise ratio (SNR) from 10.4 to 15.4 with a total increment of 50%. The total on-chip power is 0.198 W.
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Van Biesen, J. "Measurements of Signal-to-Noise Ratio (SNR) of Industrial X-Ray Film." In Non-Destructive Testing, 117–19. Elsevier, 1989. http://dx.doi.org/10.1016/b978-0-444-87450-4.50030-8.

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Gautam, Alka, Hoon-Jae Lee, and Wan-Young Chung. "ECG Signal De-Noising with Asynchronous Averaging and Filtering Algorithm." In Advancing Technologies and Intelligence in Healthcare and Clinical Environments Breakthroughs, 199–205. IGI Global, 2012. http://dx.doi.org/10.4018/978-1-4666-1755-1.ch014.

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In this study, a new algorithm is proposed—Asynchronous Averaging and Filtering (AAF) for ECG signal de-noising. R-peaks are detected with another proposed algorithm—Minimum Slot and Maximum Point selecting method (MSMP). AAF algorithm reduces random noise (major component of EMG noise) from ECG signal and provides comparatively good results for baseline wander noise cancellation. Signal to noise ratio (SNR) improves in filtered ECG signal, while signal shape remains undistorted. The authors conclude that R-peak detection with MSMP method gives comparable results from existing algorithm like Pan-Tomkins algorithm. AAF algorithm is advantageous over adaptation algorithms like Wiener and LMS algorithm. Overall performance of proposed algorithms is comparatively good.
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Ranjan Dwibedi, Sudhansu, and Bijay Kumar Ekka. "Improvement of SNR by Removing Power-line Interference from Noisy ECG Signal." In Artificial Intelligence and Communication Technologies, 591–99. 2022nd ed. Soft Computing Research Society, 2022. http://dx.doi.org/10.52458/978-81-955020-5-9-57.

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The electrical signals are invariably accompanied by components that are unrelated to the phenomenon being studied. Spurious or unwanted signal components which may occur at any frequency within band pass of the system are known as noise. The instruments are designed in such a way that the noise is minimized to facilitate accurate and sensitive measurement. Noise in Electrocardiograph (ECG) recording occurs due to high frequency Electromagnetic (EM) noise, motion artifacts and power line (50Hz or 60Hz) interference. For extraction of information from noisy signals, it is essential to increase or improve Signal to Noise ratio (SNR) which ultimately improves the performance. To process the signal waveform without distortion, the band pass of the system must be introduced such that all the frequency components of the signal contribute to signal strength. Most signal conditioners include low-pass filters designed specifically to provide maximum rejection of 50Hz noise. Such filters are called “notch filters”. Notch filters having only zeros in the transfer function has very wide notch bandwidth and offers more attenuation at adjacent frequencies. In this paper, we use Notch filter with poles added at multiple points of a unit circle (0).
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Conference papers on the topic "Signal-to-noise-ratio (SNR)"

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Howe, Dennis G. "Signal-To-Noise Ratio (SNR) For Reliable Data Recording." In 30th Annual Technical Symposium, edited by Robert P. Freese, Albert A. Jamberdino, and Maarten R. de Haan. SPIE, 1987. http://dx.doi.org/10.1117/12.936846.

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Kulshreshtha, Garima, and Usha Chauhan. "Signal-to-Interference-Noise Ratio (SINR) and Signal-to-Noise Ratio (SNR) Improvement in the Massive Multiple-Input Multiple-Output (MIMO) Systems." In 2020 2nd International Conference on Advances in Computing, Communication Control and Networking (ICACCCN). IEEE, 2020. http://dx.doi.org/10.1109/icacccn51052.2020.9362876.

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Chen, Lixin, and Bing T. Yang. "Signal-To-Noise Ratio (SNR) And Sensitivity Equations For An Enclosed Radiothermometer." In 29th Annual Technical Symposium, edited by Richard A. Mollicone and Irving J. Spiro. SPIE, 1985. http://dx.doi.org/10.1117/12.950691.

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Qian, Ying, and Yang Zhang. "Research on signal-to-noise ratio (SNR) affect physiological parameters in tumor." In 2010 3rd International Conference on Biomedical Engineering and Informatics (BMEI). IEEE, 2010. http://dx.doi.org/10.1109/bmei.2010.5639741.

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Eung-ju Kim, Ho-Yung Park, and Suki Kim. "Low frequency clock synchronization technique for low signal to noise ratio (SNR) signal recoery from noise environment." In 2009 16th International Conference on Digital Signal Processing (DSP). IEEE, 2009. http://dx.doi.org/10.1109/icdsp.2009.5201234.

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Lam, Huy Quoc, P. Shum, Songnian Fu, Le Nguyen Binh, and Y. D. Gong. "Series analysis of Signal to Noise Ratio (SNR) in active mode-locked lasers." In 2007 IFIP International Conference on Wireless and Optical Communications Networks. IEEE, 2007. http://dx.doi.org/10.1109/wocn.2007.4284167.

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Hamidi, Z. S., and N. N. M. Shariff. "Evaluation of signal to noise ratio (SNR) of log periodic dipole antenna (LPDA)." In 2013 IEEE Business Engineering and Industrial Applications Colloquium (BEIAC). IEEE, 2013. http://dx.doi.org/10.1109/beiac.2013.6560164.

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Lu, Xue, Chuang Li, and Xuewu Fan. "Calculation of signal-to-noise ratio (SNR) of infrared detection system based on MODTRAN model." In ISPDI 2013 - Fifth International Symposium on Photoelectronic Detection and Imaging, edited by Haimei Gong, Zelin Shi, Qian Chen, and Jin Lu. SPIE, 2013. http://dx.doi.org/10.1117/12.2033048.

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Madruga, F. J., P. Albendea, C. Ibarra-Castanedo, and J. M. López-Higuera. "Signal to noise ratio (SNR) comparison for lockin thermographic data processing methods in CFRP specimen." In 2010 Quantitative InfraRed Thermography. QIRT Council, 2010. http://dx.doi.org/10.21611/qirt.2010.079.

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Ngo, Thinh, Brian Kelley, and Paul Rad. "Deep Learning Based Prediction of Signal-to-Noise Ratio (SNR) for LTE and 5G Systems." In 2020 8th International Conference on Wireless Networks and Mobile Communications (WINCOM). IEEE, 2020. http://dx.doi.org/10.1109/wincom50532.2020.9272470.

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Reports on the topic "Signal-to-noise-ratio (SNR)"

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Quinn, Meghan. Geotechnical effects on fiber optic distributed acoustic sensing performance. Engineer Research and Development Center (U.S.), July 2021. http://dx.doi.org/10.21079/11681/41325.

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Distributed Acoustic Sensing (DAS) is a fiber optic sensing system that is used for vibration monitoring. At a minimum, DAS is composed of a fiber optic cable and an optic analyzer called an interrogator. The oil and gas industry has used DAS for over a decade to monitor infrastructure such as pipelines for leaks, and in recent years changes in DAS performance over time have been observed for DAS arrays that are buried in the ground. This dissertation investigates the effect that soil type, soil temperature, soil moisture, time in-situ, and vehicle loading have on DAS performance for fiber optic cables buried in soil. This was accomplished through a field testing program involving two newly installed DAS arrays. For the first installation, a new portion of DAS array was added to an existing DAS array installed a decade prior. The new portion of the DAS array was installed in four different soil types: native fill, sand, gravel, and an excavatable flowable fill. Soil moisture and temperature sensors were buried adjacent to the fiber optic cable to monitor seasonal environmental changes over time. Periodic impact testing was performed at set locations along the DAS array for over one year. A second, temporary DAS array was installed to test the effect of vehicle loading on DAS performance. Signal to Noise Ratio (SNR) of the DAS response was used for all the tests to evaluate the system performance. The results of the impact testing program indicated that the portions of the array in gravel performed more consistently over time. Changes in soil moisture or soil temperature did not appear to affect DAS performance. The results also indicated that time DAS performance does change somewhat over time. Performance variance increased in new portions of array in all material types through time. The SNR in portions of the DAS array in native silty sand material dropped slightly, while the SNR in portions of the array in sand fill and flowable fill material decreased significantly over time. This significant change in performance occurred while testing halted from March 2020 to August 2020 due to the Covid-19 pandemic. These significant changes in performance were observed in the new portion of test bed, while the performance of the prior installation remained consistent. It may be that, after some time in-situ, SNR in a DAS array will reach a steady state. Though it is unfortunate that testing was on pause while changes in DAS performance developed, the observed changes emphasize the potential of DAS to be used for infrastructure change-detection monitoring. In the temporary test bed, increasing vehicle loads were observed to increase DAS performance, although there was considerable variability in the measured SNR. The significant variation in DAS response is likely due to various industrial activities on-site and some disturbance to the array while on-boarding and off-boarding vehicles. The results of this experiment indicated that the presence of load on less than 10% of an array channel length may improve DAS performance. Overall, this dissertation provides guidance that can help inform the civil engineering community with respect to installation design recommendations related to DAS used for infrastructure monitoring.
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