Articoli di riviste: "Signal to noise ratio"

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S. Ashwin, J., e N. Manoharan. "Audio Denoising Based on Short Time Fourier Transform". Indonesian Journal of Electrical Engineering and Computer Science 9, n. 1 (1 gennaio 2018): 89. http://dx.doi.org/10.11591/ijeecs.v9.i1.pp89-92.

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<p>This paper presents a novel audio de-noising scheme in a given speech signal. The recovery of original from the communication channel without any noise is a difficult task. Many de-noising techniques have been proposed for the removal of noises from a digital signal. In this paper, an audio de-noising technique based on Short Time Fourier Transform (STFT) is implemented. The proposed architecture uses a novel approach to estimate environmental noise from speech adaptively. Here original speech signals are given as input signal. Using AWGN, noises are added to the signal. Then noised signals are de-noised using STFT techniques. Finally Signal to Noise Ratio (SNR), Peak Signal to Noise Ratio (PSNR) values for noised and de-noised signals are obtained.</p>

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Yang, Ren Di, e Yan Li Zhang. "Denoising of ECG Signal Based on Empirical Mode Decomposition and Adaptive Noise Cancellation". Applied Mechanics and Materials 40-41 (novembre 2010): 140–45. http://dx.doi.org/10.4028/www.scientific.net/amm.40-41.140.

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To remove the noises in ECG and to overcome the disadvantage of the denoising method only based on empirical mode decomposition (EMD), a combination of EMD and adaptive noise cancellation is introduced in this paper. The noisy ECG signals are firstly decomposed into intrinsic mode functions (IMFs) by EMD. Then the IMFs corresponding to noises are used to reconstruct signal. The reconstructed signal as the reference input of adaptive noise cancellation and the noisy ECG as the basic input, the de-noised ECG signal is obtained after adaptive filtering. The de-noised ECG has high signal-to-noise ratio, preferable correlation coefficient and lower mean square error. Through analyzing these performance parameters and testing the denoising method using MIT-BIH Database, the conclusion can be drawn that the combination of EMD and adaptive noise cancellation has considered the frequency distribution of ECG and noises, eliminate the noises effectively and need not to select a proper threshold.

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

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Selvaraj, Poovarasan, e E. Chandra. "Ideal ratio mask estimation using supervised DNN approach for target speech signal enhancement". Journal of Intelligent & Fuzzy Systems 42, n. 3 (2 febbraio 2022): 1869–83. http://dx.doi.org/10.3233/jifs-211236.

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The most challenging process in recent Speech Enhancement (SE) systems is to exclude the non-stationary noises and additive white Gaussian noise in real-time applications. Several SE techniques suggested were not successful in real-time scenarios to eliminate noises in the speech signals due to the high utilization of resources. So, a Sliding Window Empirical Mode Decomposition including a Variant of Variational Model Decomposition and Hurst (SWEMD-VVMDH) technique was developed for minimizing the difficulty in real-time applications. But this is the statistical framework that takes a long time for computations. Hence in this article, this SWEMD-VVMDH technique is extended using Deep Neural Network (DNN) that learns the decomposed speech signals via SWEMD-VVMDH efficiently to achieve SE. At first, the noisy speech signals are decomposed into Intrinsic Mode Functions (IMFs) by the SWEMD Hurst (SWEMDH) technique. Then, the Time-Delay Estimation (TDE)-based VVMD was performed on the IMFs to elect the most relevant IMFs according to the Hurst exponent and lessen the low- as well as high-frequency noise elements in the speech signal. For each signal frame, the target features are chosen and fed to the DNN that learns these features to estimate the Ideal Ratio Mask (IRM) in a supervised manner. The abilities of DNN are enhanced for the categories of background noise, and the Signal-to-Noise Ratio (SNR) of the speech signals. Also, the noise category dimension and the SNR dimension are chosen for training and testing manifold DNNs since these are dimensions often taken into account for the SE systems. Further, the IRM in each frequency channel for all noisy signal samples is concatenated to reconstruct the noiseless speech signal. At last, the experimental outcomes exhibit considerable improvement in SE under different categories of noises.

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

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6

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

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Najafipour, Abbas, Abbas Babaee e S. Mohammad Shahrtash. "Comparing the trustworthiness of signal-to-noise ratio and peak signal-to-noise ratio in processing noisy partial discharge signals". IET Science, Measurement & Technology 7, n. 2 (1 marzo 2013): 112–18. http://dx.doi.org/10.1049/iet-smt.2012.0113.

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

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

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

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

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

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Davidson, Steven J. "The Signal-to-Noise Ratio". Emergency Medicine News 26, n. 8 (agosto 2004): 38. http://dx.doi.org/10.1097/00132981-200408000-00023.

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Gudiškis, Andrius. "HEART BEAT DETECTION IN NOISY ECG SIGNALS USING STATISTICAL ANALYSIS OF THE AUTOMATICALLY DETECTED ANNOTATIONS / ŠIRDIES DŪŽIŲ NUSTATYMAS IŠ IŠKRAIPYTŲ EKG SIGNALŲ ATLIEKANT AUTOMATIŠKAI APTIKTŲ ATSKAITŲ STATISTINĘ ANALIZĘ". Mokslas – Lietuvos ateitis 7, n. 3 (13 luglio 2015): 300–303. http://dx.doi.org/10.3846/mla.2015.787.

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This paper proposes an algorithm to reduce the noise distortion influence in heartbeat annotation detection in electrocardiogram (ECG) signals. Boundary estimation module is based on energy detector. Heartbeat detection is usually performed by QRS detectors that are able to find QRS regions in a ECG signal that are a direct representation of a heartbeat. However, QRS performs as intended only in cases where ECG signals have high signal to noise ratio, when there are more noticeable signal distortion detectors accuracy decreases. Proposed algorithm uses additional data, taken from arterial blood pressure signal which was recorded in parallel to ECG signal, and uses it to support the QRS detection process in distorted signal areas. Proposed algorithm performs as well as classical QRS detectors in cases where signal to noise ratio is high, compared to the heartbeat annotations provided by experts. In signals with considerably lower signal to noise ratio proposed algorithm improved the detection accuracy to up to 6%. Širdies ritmas yra vienas svarbiausių ir daugiausia informacijos apie pacientų būklę teikiančių fiziologinių parametrų. Širdies ritmas nustatomas iš elektrokardiogramos (EKG), atliekant QRS regionų, kurie yra interpretuojami kaip širdies dūžio ãtskaitos, paiešką. QRS regionų aptikimas yra klasikinis uždavinys, nagrinėjamas jau keletą dešimtmečių, todėl širdies dūžių nustatymo iš EKG signalų metodų yra labai daug. Deja, šie metodai tikslūs ir patikimi tik esant dideliam signalo ir triukšmo santykiui. Kai EKG signalai labai iškraipomi, QRS aptiktuvai ne visada gali atskirti QRS regioną, o kartais jį randa ten, kur iš tikro jo būti neturėtų. Straipsnyje siūlomas algoritmas, kurį taikant sumažinama triukšmo įtaka nustatant iš EKG signalų QRS regionus. Tam naudojamas QRS aptiktuvas, kartu prognozuojantis širdies dūžio atskaitą. Remiamasi arterinio kraujo spaudimo signalo duomenimis, renkama atskaitų statistika ir atliekama jos analizė.

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CHAPEAU-BLONDEAU, FRANÇOIS, e JULIO ROJAS-VARELA. "NONLINEAR SIGNAL PROPAGATION ENHANCED BY NOISE VIA STOCHASTIC RESONANCE". International Journal of Bifurcation and Chaos 10, n. 08 (agosto 2000): 1951–59. http://dx.doi.org/10.1142/s0218127400001249.

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A model is developed for a nonlinear line of coupled noisy threshold elements. The propagation on the line of various information-carrying signals, periodic, aperiodic or random, is analyzed. Different measures quantifying the efficacy of the propagation are calculated, including signal-to-noise ratio, cross-correlation measures, information-theoretic measures and propagation length. These measures are shown to be improvable by the addition of noise. These results establish a new instance of the nonlinear phenomenon of stochastic resonance under the form of a noise-enhanced propagation applying to a broad variety of signals and noises. The results also contain significance for the propagation of neuronal signals.

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Han, Xiao Gang, Mei Quan Liu e Qin Lei Sun. "The Design and Implementation of Adaptive Noise Cancellation for Magnetic Flux Leakage Test Signal". Applied Mechanics and Materials 441 (dicembre 2013): 393–96. http://dx.doi.org/10.4028/www.scientific.net/amm.441.393.

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In the application of magnetic flux leakage (MFL) nondestructive testing, the signal will be disturbed by varying noises. It seriously affects the accuracy of detection judgment result. This paper describes the design of least mean square (LMS) noise cancellation for MFL signal and the implementation based on ARM platform. Two giant magnetoresistive sensors are used to measure the signal, one sensor for MFL signal (with noise) and the other one for the noise signal. They are inputted to the noise cancellation to obtain pure MFL signals. Experimental results show that the LMS noise cancellation significantly improves the signal to noise ratio.

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Rao*, G. Manmadha, Raidu Babu D.N, Krishna Kanth P.S.L, Vinay B. e Nikhil V. "Reduction of Impulsive Noise from Speech and Audio Signals by using Sd-Rom Algorithm". International Journal of Recent Technology and Engineering 10, n. 1 (30 maggio 2021): 265–68. http://dx.doi.org/10.35940/ijrte.a5943.0510121.

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Removal of noise is the heart for speech and audio signal processing. Impulse noise is one of the most important noise which corrupts different parts in speech and audio signals. To remove this type of noise from speech and audio signals the technique proposed in this work is signal dependent rank order mean (SD-ROM) method in recursive version. This technique is used to replace the impulse noise samples based on the neighbouring samples. It detects the impulse noise samples based on the rank ordered differences with threshold values. This technique doesn’t change the features and tonal quality of signal. Rank ordered differences is used for detecting the impulse noise samples in speech and audio signals. Once the sample is detected as corrupted sample, that sample is replaced with rank ordered mean value and this rank ordered mean value depends on the sliding window size and neighbouring samples. This technique shows good results in terms of signal to noise ratio (SNR) and peak signal to noise ratio (PSNR) when compared with other techniques. It mainly used for removal of impulse noises from speech and audio signals.

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Zhang, Beiming, Guoping Chen e Chun Jiang. "Research on Modulation Recognition Method in Low SNR Based on LSTM". Journal of Physics: Conference Series 2189, n. 1 (1 febbraio 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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Lan, Jin Hui, e Zhi Cheng Liu. "A Novel Noise Elimination Method for MEMS Sensor". Key Engineering Materials 483 (giugno 2011): 779–83. http://dx.doi.org/10.4028/www.scientific.net/kem.483.779.

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It is inevitable that measured signals are contaminated with noise when MEMS sensor is used for an outdoor measurement. So the obtained signals need noise elimination, which is one of key technologies for signal conditioning. In this paper, a novel noise elimination method based on algorithm of blind source separation (BSS) for MEMS sensor is proposed to separate the source signals from the mixed signals with noises. The BSS algorithm based on maximum signal noise ratio (SNR) is a method of global optimal property, using the characteristic that SNR is maximal when statistically independent source signals are completely separated.The algorithm has a low computational complexity for instantaneous linear mixture signals. It is effective to acquire the source signals from the mixed signals with noises obtained by MEMS sensor,and is successfully verified by simulation experiment of BSS algorithm.The result of noise elimination experiment has achieved signal conditioning and eliminates ambient noises for MEMS sensor signals.

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Zhang, Dengyong, Shanshan Wang, Feng Li, Jin Wang, Arun Kumar Sangaiah, Victor S. Sheng e Xiangling Ding. "An ECG Signal De-Noising Approach Based on Wavelet Energy and Sub-Band Smoothing Filter". Applied Sciences 9, n. 22 (18 novembre 2019): 4968. http://dx.doi.org/10.3390/app9224968.

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Electrocardiographic (ECG) signal is essential to diagnose and analyse cardiac disease. However, ECG signals are susceptible to be contaminated with various noises, which affect the application value of ECG signals. In this paper, we propose an ECG signal de-noising method using wavelet energy and a sub-band smoothing filter. Unlike the traditional wavelet threshold de-noising method, which carries out threshold processing for all wavelet coefficients, the wavelet coefficients that require threshold de-noising are selected according to the wavelet energy and other wavelet coefficients remain unchanged in the proposed method. Moreover, The sub-band smoothing filter is adopted to further de-noise the ECG signal and improve the ECG signal quality. The ECG signals of the standard MIT-BIH database are adopted to verify the proposed method using MATLAB software. The performance of the proposed approach is assessed using Signal-To-Noise ratio (SNR), Mean Square Error (MSE) and percent root mean square difference (PRD). The experimental results illustrate that the proposed method can effectively remove noise from the noisy ECG signals in comparison to the existing methods.

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Chernoyarov, Oleg, Alexey Glushkov, Vladimir Litvinenko, Yuliya Litvinenko e Serguei Pergamenshchikov. "Signal-to-noise ratio measurement for the signals with constant amplitude". Journal of Physics: Conference Series 2388, n. 1 (1 dicembre 2022): 012072. http://dx.doi.org/10.1088/1742-6596/2388/1/012072.

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Abstract A digital meter measuring the signal-to-noise ratio in the channel for transmitting discrete information with a constant amplitude of symbols that are the phase or frequency shift keyed signals is proposed. The desired value of the signal-to-noise ratio is determined based on the number of exceedings of the threshold level by the modulus of the logarithm of the ratio of the amplitudes of the received and previous symbols in the sample of a specified size, while the meter itself is invariant to the absolute levels of the signal and noise. It is shown that in the channel with Gaussian noise, this number depends only on the desired signal-to-noise ratio. The necessary calculation relations are successfully obtained; it is also established that the Gaussian approximation of the symbol amplitude probability density is valid. Statistical simulation of the meter operation has been carried out for receiving binary phase shift keyed signals. It is proven that the given theoretical meter characteristics are in satisfactory agreement with the corresponding experimental data and the small measurement error is provided within a wide range of signal-to-noise ratio values.

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Kovtun, S., A. Vetoshkin e V. Kutsenko. "INTERFERENCE RESISTANCE OF A CORRELATION RECEIVER AT A LIMITED OBSERVATION INTERVAL". Наукові праці Державного науково-дослідного інституту випробувань і сертифікації озброєння та військової техніки 14, n. 4 (30 dicembre 2022): 51–58. http://dx.doi.org/10.37701/dndivsovt.14.2022.06.

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The article considers the possibility of increasing the capability of detecting a useful radio signal against the background of noise and simultaneously reducing the possibility of mistaking noise fluctuations for a signal. Moreover, if it is absent then the Neumann-Pearson statistical test should be used. This criterion is a partial case from the class of criteria consisting in the calculation of the likelihood ratio and does not require knowledge of a priori probabilities of the presence or absence a useful signal while constructing an optimal receiver and Bayesian methods are used. The effectiveness of the detection procedure according to the Neumann-Pearson test is characterized by the probability of correct detection at a fixed false alarm probability. These probabilities depend on the signal-to-noise ratio at the output of the detection device. So, an important preparatory stage of the reception immunity analysis is the study of this parameter which is very important in the section of statistical radio engineering. It is known that the optimal detection of a signal against a background of white noise is reduced to the correlation integral calculation. This calculation can be done directly by using a multiplier and integrator (correlation method), by using a filter that is optimal for this signal (filter method) or by using a correlation-filter method. The presence of a priori uncertainty during signal reception makes it impossible to create an optimal (consistent) receiving path and therefore makes it impossible to obtain the required value of the signal-to-noise ratio at the output of the detection device. In this case, an autocorrelation algorithm is used which is resistant to a priori uncertainty of signal parameters and is “optimal” while detecting signals of unknown shape. The most general character for determining the signal/noise ratio has an expression for the case of an incoherent single-channel autocorrelation algorithm for detecting a noisy signal against a background of stationary white noise (interference). For example, quite a large number of scientific works are devoted to the interference immunity issue of the correlation reception method. They mainly consider the interference immunity of correlation receivers when a mixture of harmonic signal and noise passes through them. The circuits with the same average frequencies of multiplying processes and etc. are analyzed. There are also known works in which formulas relating the value of the signal-to-noise ratio at the output of the receiving path for a complex signal to the input signal-to-noise ratio are given. In the above works, idealized cases are considered, namely, when the observation is conducted on a fairly long observation interval but the real observation interval is quite limited. In this case, the averaging is carried out over a limited time interval and it is necessary to take into account the intercorrelation relations between the signal and noise, therefore, along with the signal-signal, noise-noise, signal-noise and noise-signal components, we will have the following mutual correlation moments between the combinations of signal and noise component. The issue of an autocorrelation receiver when passing an additive mixture of signal and white noise at a limited observation interval is investigated in which the immunity index is considered and a general solution of the signal/noise ratio based on the deviation criterion is obtained. Therefore, obtaining an analytical expression that establishes the relationship between the signal-to-noise ratio at the input and output of the receiver is very important and relevant. The purpose of the article is to obtain an analytical expression of the signal-to-noise ratio value at the threshold device input of an incoherent autocorrelation receiver at a limited observation interval.

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Bosworth, B. T., W. R. Bernecky, J. D. Nickila, B. Adal e G. C. Carter. "Estimating Signal-to-Noise Ratio (SNR)". IEEE Journal of Oceanic Engineering 33, n. 4 (ottobre 2008): 414–18. http://dx.doi.org/10.1109/joe.2008.2001780.

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Dhara, Asish K. "Enhancement of signal-to-noise ratio". Journal of Statistical Physics 87, n. 1-2 (aprile 1997): 251–71. http://dx.doi.org/10.1007/bf02181487.

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Schultz, Simon. "Signal-to-noise ratio in neuroscience". Scholarpedia 2, n. 6 (2007): 2046. http://dx.doi.org/10.4249/scholarpedia.2046.

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Redpath, T. W. "Signal-to-noise ratio in MRI." British Journal of Radiology 71, n. 847 (luglio 1998): 704–7. http://dx.doi.org/10.1259/bjr.71.847.9771379.

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Lin, Tingting, Xiaokang Yao, Sijia Yu e Yang Zhang. "Electromagnetic Noise Suppression of Magnetic Resonance Sounding Combined with Data Acquisition and Multi-Frame Spectral Subtraction in the Frequency Domain". Electronics 9, n. 8 (5 agosto 2020): 1254. http://dx.doi.org/10.3390/electronics9081254.

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As an advanced groundwater detection method, magnetic resonance sounding (MRS) has received more and more attention. However, the biggest challenge is that MRS measurements always suffer with a bad signal-to-noise ratio (SNR). Aiming at the problem of noise interference in MRS measurement, we propose a novel noise-suppression approach based on the combination of data acquisition and multi-frame spectral subtraction (DA-MFSS). The pure ambient noise from the measurement area is first collected by the receiving coil, and then the noisy MRS signal is recorded following the pulse moments transmitting. The procedure of the pure noise and the noisy MRS signal acquisition will be repeated several times. Then, the pure noise and the noisy signal are averaged to preliminarily suppress the noise. Secondly, the averaged pure noise and the noisy signal are divided into multiple frames. The framed signal is transformed into the frequency domain and the spectral subtraction method is applied to further suppress the electromagnetic noise embedded in the noisy MRS signal. Finally, the de-noised signal is recovered by the overlap-add method and inverse Fourier transformation. The approach was examined by numerical simulation and field measurements. After applying the proposed approach, the SNR of the MRS data was improved by 16.89 dB and both the random noise and the harmonic noise were well suppressed.

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Sodickson, Daniel K., Mark A. Griswold, Peter M. Jakob, Robert R. Edelman e Warren J. Manning. "Signal-to-noise ratio and signal-to-noise efficiency in SMASH imaging". Magnetic Resonance in Medicine 41, n. 5 (maggio 1999): 1009–22. http://dx.doi.org/10.1002/(sici)1522-2594(199905)41:5<1009::aid-mrm21>3.0.co;2-4.

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Choli, Morwan, Peter M. Jakob, Ralf B. Loeffler e Claudia M. Hillenbrand. "Mixed-bandwidth acquisitions: Signal-to-noise ratio and signal-to-noise efficiency". Journal of Magnetic Resonance Imaging 32, n. 4 (29 settembre 2010): 997–1002. http://dx.doi.org/10.1002/jmri.22327.

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Peng, Liang, Shengliang Fang, Youchen Fan, Mengtao Wang e Zhao Ma. "A Method of Noise Reduction for Radio Communication Signal Based on RaGAN". Sensors 23, n. 1 (1 gennaio 2023): 475. http://dx.doi.org/10.3390/s23010475.

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Radio signals are polluted by noise in the process of channel transmission, which will lead to signal distortion. Noise reduction of radio signals is an effective means to eliminate the impact of noise. Using deep learning (DL) to denoise signals can reduce the dependence on artificial domain knowledge, while traditional signal-processing-based denoising methods often require knowledge of the artificial domain. Aiming at the problem of noise reduction of radio communication signals, a radio communication signal denoising method based on the relativistic average generative adversarial networks (RaGAN) is proposed in this paper. This method combines the bidirectional long short-term memory (Bi-LSTM) model, which is good at processing time-series data with RaGAN, and uses the weighted loss function to construct a noise reduction model suitable for radio communication signals, which realizes the end-to-end denoising of radio signals. The experimental results show that, compared with the existing methods, the proposed algorithm has significantly improved the noise reduction effect. In the case of a low signal-to-noise ratio (SNR), the signal modulation recognition accuracy is improved by about 10% after noise reduction.

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Nimmagadda, Padmaja, Kondru Ayyappa Swamy, Samuda Prathima, Sushma Chintha e Zachariah Callottu Alex. "Short-term uncleaned signal to noise threshold ratio based end-to-end time domain speech enhancement in digital hearing aids". Indonesian Journal of Electrical Engineering and Computer Science 27, n. 1 (1 luglio 2022): 131. http://dx.doi.org/10.11591/ijeecs.v27.i1.pp131-138.

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This paper presents the improvements in the combined solution for the noise estimation and the speech enhancement in digital hearing aids in time domain. This study focuses on the single channel statistical temporal speech enhancement using adaptive Wiener filtering. In this technique, the noise is updated based on the short-term uncleaned signal to noise threshold ratio (ST-USNTR) of the frame. It works best if and only if the back ground noise level is low compared to that of speech of interest. We considered the time domain algorithms in order to consider the time varying nature of speech signal. The performance of the proposed algorithm is evaluated for speech signal with seven ty pes of noises and three signal to noise ratios (SNR) levels in each type of noise. From the results, it is clear that the basic level of adaptive speech enhancement is obtained using statistical parameters of noisy speech without the need for reference input.

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Degtyaryov, Alexander, e Alexander Kozhemyakin. "Matched Filter Design Method for White Noise and Non-Gaussian Noise Conditions". Infocommunications and Radio Technologies 5, n. 2 (7 ottobre 2022): 253–59. http://dx.doi.org/10.29039/2587-9936.2022.05.2.19.

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A method for synthesizing a matched filter for conditions of simultaneous action of white noise and non-Gaussian noise is proposed. The method is based on the representation of the matched filter as a linear system that implements an integral transformation with a real kernel. Noise, non-Gaussian noise and signal are considered to be frequency limited. The signal-to-noise ratio at the reference point in time is determined. The expansion of the correlation function of the interference and the signal into a series of count functions and the resulting signal-to-noise ratio give an equation that allows one to determine the signals that maximize the signal-to-noise ratio.

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31

Fan, Yi Bo, Feng Shou Gu e Andrew Ball. "Acoustic Emission Monitoring of Mechanical Seals Using MUSIC Algorithm Based on Higher Order Statistics". Key Engineering Materials 413-414 (giugno 2009): 811–16. http://dx.doi.org/10.4028/www.scientific.net/kem.413-414.811.

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This paper presents the use of the MUSIC algorithm improved by higher order statistics (HOS) to extract key features from the noisy acoustic emission (AE) signals. The low signal-to-noise ratio of AE signals has been identified as a main barrier to the successful condition monitoring of pump mechanical seals. Since HOS methods can effectively eliminate Gaussian noise, it is possible in theory to identify a change in seal conditions from AE measurements even with low signal-to-noise ratios. Tests conducted on a test rig show that the developed algorithm can successfully detect the AE signal generated from the friction of seal faces under noisy conditions.

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Khan, Junaid Bahadar, Tariqullah Jan, Ruhul Amin Khalil, Nasir Saeed e Muhannad Almutiry. "An Efficient Multistage Approach for Blind Source Separation of Noisy Convolutive Speech Mixture". Applied Sciences 11, n. 13 (27 giugno 2021): 5968. http://dx.doi.org/10.3390/app11135968.

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This paper proposes a novel efficient multistage algorithm to extract source speech signals from a noisy convolutive mixture. The proposed approach comprises two stages named Blind Source Separation (BSS) and de-noising. A hybrid source prior model separates the source signals from the noisy reverberant mixture in the BSS stage. Moreover, we model the low- and high-energy components by generalized multivariate Gaussian and super-Gaussian models, respectively. We use Minimum Mean Square Error (MMSE) to reduce noise in the noisy convolutive mixture signal in the de-noising stage. Furthermore, the two proposed models investigate the performance gain. In the first model, the speech signal is separated from the observed noisy convolutive mixture in the BSS stage, followed by suppression of noise in the estimated source signals in the de-noising module. In the second approach, the noise is reduced using the MMSE filtering technique in the received noisy convolutive mixture at the de-noising stage, followed by separation of source signals from the de-noised reverberant mixture at the BSS stage. We evaluate the performance of the proposed scheme in terms of signal-to-distortion ratio (SDR) with respect to other well-known multistage BSS methods. The results show the superior performance of the proposed algorithm over the other state-of-the-art methods.

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Cunbao Lin, Cunbao Lin, Shuhua Yan Shuhua Yan, Zhiguang Du Zhiguang Du, Guochao Wang Guochao Wang e Chunhua Wei Chunhua Wei. "Symmetrical short-period and high signal-to-noise ratio heterodyne grating interferometer". Chinese Optics Letters 13, n. 10 (2015): 100501–5. http://dx.doi.org/10.3788/col201513.100501.

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Kolar, Petar, Lovro Blažok e Dario Bojanjac. "NMR spectroscopy threshold signal-to-noise ratio". tm - Technisches Messen 88, n. 9 (17 aprile 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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Li, Jian Jun, e Jian Feng Zhao. "Detection of Life Characteristic Signals Based on High Order Statistics". Applied Mechanics and Materials 239-240 (dicembre 2012): 807–10. http://dx.doi.org/10.4028/www.scientific.net/amm.239-240.807.

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Life parameters signal has characteristics of extremely low frequency, low signal-to-noise ratio, and the easy submerged in strong clutter noises. The method for detecting life signal based on filter bank and high order statistics is presented, in which neither the Gaussian supposition of the observed signal, nor a prior information about the waveform and arrival time of the observed signal is necessary. The principle of method is to separate the spectrum of input signal into many narrow frequency bands, whose Sub-band signal is followed by a short-time estimation of higher-order statistics so as to suppress Gaussian noises. Simulated results show that the method not only can completely descript life signals in the time-frequency domain, but improve the signal-to-noise ratio and the ability of detecting algorithm. Moreover, the method is effective and practical.

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36

Shinpaugh, K. A., R. L. Simpson, A. L. Wicks, S. M. Ha e J. L. Fleming. "Signal-processing techniques for low signal-to-noise ratio laser Doppler velocimetry signals". Experiments in Fluids 12-12, n. 4-5 (marzo 1992): 319–28. http://dx.doi.org/10.1007/bf00187310.

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37

Fathima, S. Yasmin, G. V. S. Karthik, M. Zia Ur Rahman e A. Lay-Ekuakille. "Efficient Artifact Elimination in Cardiac Signals using Variable Step Size Adaptive Noise Cancellers". International Journal of Measurement Technologies and Instrumentation Engineering 2, n. 1 (gennaio 2012): 35–51. http://dx.doi.org/10.4018/ijmtie.2012010103.

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In this paper several variable step size adaptive filter structures for extracting high resolution electrocardiographic (ECG) signals are presented which estimates the deterministic components of the ECG signal and removes the artifacts. The noise canceller minimizes the mean square error (MSE) between the input noisy ECG signal and noise reference. Different noise canceller structures are proposed to remove diverse forms of artifacts: power line interference, baseline wander, muscle artifacts and electrode motion artifacts. The proposed implementation is suitable real time applications, where large signal to noise ratios with fast convergence are required. Simulation studies shows that the proposed realization gives better performance compared to existing realizations in terms of signal to noise ratio, convergence rate and MSE.

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Hasan, Fadhil Sahib. "Chaotic signals denoising using empirical mode decomposition inspired by multivariate denoising". International Journal of Electrical and Computer Engineering (IJECE) 10, n. 2 (1 aprile 2020): 1352. http://dx.doi.org/10.11591/ijece.v10i2.pp1352-1358.

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Empirical mode decomposition (EMD) is an effective noise reduction method to enhance the noisy chaotic signal over additive noise. In this paper, the intrinsic mode functions (IMFs) generated by EMD are thresholded using multivariate denoising. Multivariate denoising is multivariable denosing algorithm that is combined wavelet transform and principal component analysis to denoise multivariate signals in adaptive way. The proposed method is compared at a various signal to noise ratios (SNRs) with different techniques and different types of noise. Also, scale dependent Lyapunov exponent (SDLE) is used to test the behavior of the denoised chaotic signal comparing with clean signal. The results show that EMD-MD method has the best root mean square error (RMSE) and signal to noise ratio gain (SNRG) comparing with the conventional methods.

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Zhang, Yatao, Shoushui Wei, Yutao Long e Chengyu Liu. "Performance Analysis of Multiscale Entropy for the Assessment of ECG Signal Quality". Journal of Electrical and Computer Engineering 2015 (2015): 1–9. http://dx.doi.org/10.1155/2015/563915.

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This study explored the performance of multiscale entropy (MSE) for the assessment of mobile ECG signal quality, aiming to provide a reasonable application guideline. Firstly, the MSE for the typical noises, that is, high frequency (HF) noise, low frequency (LF) noise, and power-line (PL) noise, was analyzed. The sensitivity of MSE to the signal to noise ratio (SNR) of the synthetic artificial ECG plus different noises was further investigated. The results showed that the MSE values could reflect content level of various noises contained in the ECG signals. For the synthetic ECG plus LF noise, the MSE was sensitive to SNR within higher range of scale factor. However, for the synthetic ECG plus HF noise, the MSE was sensitive to SNR within lower range of scale factor. Thus, a recommended scale factor range within 5 to 10 was given. Finally, the results were verified on the real ECG signals, which were derived from MIT-BIH Arrhythmia Database and Noise Stress Test Database. In all, MSE could effectively assess the noise level on the real ECG signals, and this study provided a valuable reference for applying MSE method to the practical signal quality assessment of mobile ECG.

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Li, Rong, Guang-Rong Qin, Gang Hu, Xiao-Dong Wen e Heng-Jing Zhu. "Signal-to-Noise Ratio in Bistable System Subject to Signal and Monochromatic Noise". Communications in Theoretical Physics 24, n. 1 (30 luglio 1995): 19–26. http://dx.doi.org/10.1088/0253-6102/24/1/19.

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41

Bohachev, Ihor, Svitlana Kovtun, Yurii Kuts, Stanislav Sozonov e Vladyslav Khaidurov. "Enhanced phase method of signal detection for ultrasonic magnetostriction defectoscopy of power equipment". System Research in Energy 2023, n. 2 (9 giugno 2023): 72–82. http://dx.doi.org/10.15407/srenergy2023.02.072.

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The magnetostrictive method of ultrasonic flaw detection has certain advantages, in particular, the ability to control objects with complex geometry, at their high temperature, the ability to control dry contact between the transducer and the object, difficult access to the controlled area, etc. The peculiarities of the generation of ultrasonic waves by magnetostrictive transducers and their distribution in control objects determine the low level of the signal/noise ratio, which limits the possibilities of practical application of this method. The aim of the paper is to improve the phase method for detecting signals of magnetostrictive receivers with a low signal/noise ratio and to check the effectiveness of the proposed technical solution for solving problems of non-destructive testing of energy equipment elements using magnetostrictive defectoscopes. The paper discusses the phase method of detecting radio pulse signals of magnetostrictive converters against the background of additive noise, which is based on a combination of the capabilities of the discrete Hilbert transformation, which makes it possible to determine the envelope and phase of signals, and methods of statistical processing of the results of phase measurements. The proposed signal processing algorithm was studied both in a model experiment and when processing real magnetostrictive defectoscope signals. The proposed method makes it possible to detect radio pulse signals with a signal/noise ratio close to 1. The reliability of the obtained data is confirmed by the results of computer simulation. The considered method of detecting signals can be used in ultrasonic magnetostrictive defectoscopes and other diagnostic systems operating in conditions of reduced signal/noise ratio. Keywords: magnetostrictive defectoscope, ultrasonic defectoscopy, phase methods of signal processing, Hilbert transform, envelope, phase, sample resulting length of the vector.

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42

Zhang, Mei Jun, Hao Chen, Chuang Wang e Qing Cao. "Threshold Noise Reduction Research of Improved EEMD Method". Applied Mechanics and Materials 226-228 (novembre 2012): 237–40. http://dx.doi.org/10.4028/www.scientific.net/amm.226-228.237.

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In order to extract effectively detection signals in the noise background for non-stationary signal.On the basis of EEMD, improved EEMD is put forward, the improve EEMD threshold noise reduction is researched in this paper.The simulation signal compared the noise reduction effect of the wavelet,EMD,EEMD,and the improved EEMD. The improved EEMD threshold noise reduction have the best noise reduction result , the highest signal-to-noise ratio, the smallest standard deviation error.After the improved EEMD threshold noise reduction , the measurement signal time domain waveform smooth. More high frequency noise was obviously reduced in Hilbert time- frequency spectrum. Signal-to-noise ratio significantly improve, and signal characteristics are very clear.

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Rouis, Mohamed, Abdelkrim Ouafi e Salim Sbaa. "Optimal level and order detection in wavelet decomposition for PCG signal denoising". Biomedical Engineering / Biomedizinische Technik 64, n. 2 (24 aprile 2019): 163–76. http://dx.doi.org/10.1515/bmt-2018-0001.

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Abstract The recorded phonocardiogram (PCG) signal is often contaminated by different types of noises that can be seen in the frequency band of the PCG signal, which may change the characteristics of this signal. Discrete wavelet transform (DWT) has become one of the most important and powerful tools of signal representation, but its effectiveness is influenced by the issue of the selected mother wavelet and decomposition level (DL). The selection of the DL and the mother wavelet are the main challenges. This work proposes a new approach for finding an optimal DL and optimal mother wavelet for PCG signal denoising. Our approach consists of two algorithms designed to tackle the problems of noise and variability caused by PCG acquisition in a real clinical environment for different categories of patients. The results obtained are evaluated by examining the coherence analysie (Coh) correlation coefficient (Corr) and the mean square error (MSE) and signal-to-noise ratio (SNR) in simulated noisy PCG signals. The experimental results show that the proposed method can effectively reduce noise.

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CHU, PETER C., LEONID M. IVANOV e TATYANA M. MARGOLINA. "ROTATION METHOD FOR RECONSTRUCTING PROCESS AND FIELD FROM IMPERFECT DATA". International Journal of Bifurcation and Chaos 14, n. 08 (agosto 2004): 2991–97. http://dx.doi.org/10.1142/s0218127404010941.

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Reconstruction of processes and fields from noisy data is to solve a set of linear algebraic equations. Three factors affect the accuracy of reconstruction: (a) a large condition number of the coefficient matrix, (b) high noise-to-signal ratio in the source term, and (c) no a priori knowledge of noise statistics. To improve reconstruction accuracy, the set of linear algebraic equations is transformed into a new set with minimum condition number and noise-to-signal ratio using the rotation matrix. The procedure does not require any knowledge of low-order statistics of noises. Several examples including highly distorted Lorenz attractor illustrate the benefit of using this procedure.

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45

Ma, Long, Guo Xia, Shiqun Jin, Lihao Bai, Jiangtao Wang, Qiaoqin Chen e Xiaobo Cai. "Effect of Spectral Signal-to-Noise Ratio on Resolution Enhancement at Surface Plasmon Resonance". Sensors 21, n. 2 (18 gennaio 2021): 641. http://dx.doi.org/10.3390/s21020641.

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Refractive index resolution is an important indicator for a wavelength interrogation surface plasmon resonance sensor, which can be affected by signal-to-noise ratio. This paper investigates the impact of spectral signal-to-noise ratio on a surface plasmon resonance sensor. The effects of different spectral powers and noises are compared and verified through simulation and experiments. The results indicate that the optimal resonance wavelength is changed and the refractive index resolution can even be nearly twice as good when the spectral signal-to-noise ratio is increased. The optimal resonance wavelength can be found by changing the spectral power distribution or noise.

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Thakran, Snekha. "A hybrid GPFA-EEMD_Fuzzy threshold method for ECG signal de-noising". Journal of Intelligent & Fuzzy Systems 39, n. 5 (19 novembre 2020): 6773–82. http://dx.doi.org/10.3233/jifs-191518.

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The Electrocardiogram (ECG) signal records the electrical activity of the heart. It is very difficult for physicians to analyze the ECG signal if noise is embedded during acquisition to inspect the heart’s condition. The denoising of electrocardiogram signals based on the genetic particle filter algorithm(GPFA) using fuzzy thresholding and ensemble empirical mode decomposition (EEMD) is proposed in this paper, which efficiently removes noise from the ECG signal. This paper proposes a two-phase scheme for eliminating noise from the ECG signal. In the first phase, the noisy signal is decomposed into a true intrinsic mode function (IMFs) with the help of EEMD. EEMD is better than EMD because it removes the mode-mixing effect. In the second phase, IMFs which are corrupted by noise is obtained by using spectral flatness of each IMF and fuzzy thresholding. The corrupted IMFs are filtered using a GPF method to remove the noise. Then, the signal is reconstructed with the processed IMFs to get the de-noised ECG. The proposed algorithm is analyzed for a different local hospital database, and it gives better root mean square error and signal to noise ratio than other existing techniques (Wavelet transform (WT), EMD, Particle filter(PF) based method, extreme-point symmetric mode decomposition with Nonlocal Means(ESMD-NLM), and discrete wavelet with Savitzky-Golay(DW-SG) filter).

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Deo, Jaspreet Singh. "Dependency of Output Signal to Noise Ratio on Output Power and Signal to Noise Ratio at Reference Path in the Adaptive Noise Cancellation Technique". International Journal of Advance Research and Innovation 7, n. 1 (2019): 62–66. http://dx.doi.org/10.51976/ijari.711909.

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In Adaptive noise cancelling method uses two inputs one is primary input and other is the reference input. A “primary” input the combination of main signal and noise whereas a “reference” input contains correlated noise within the primary signal. The difference of reference input from primary gives the noise free signal. It is verified that signal to noise ratio at reference path impact the signal to noise ratio at output, and by decreasing the signal to noise ratio at reference path increase in signal to noise ratio achieved. Also, by decreasing changing the output power a SIR changes immensly.

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48

Rouis, Mohamed, Salim Sbaa e Nasser Edinne Benhassine. "The effectiveness of the choice of criteria on the stationary and non-stationary noise removal in the phonocardiogram (PCG) signal using discrete wavelet transform". Biomedical Engineering / Biomedizinische Technik 65, n. 3 (26 maggio 2020): 353–66. http://dx.doi.org/10.1515/bmt-2019-0197.

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AbstractThe greatest problem with recording heart sounds is parasitic noise effects. A reasonable solution to reduce noise can be carried out by minimization of extraneous noises in the vicinity of the patient during recording, in addition to the methods of signal processing that must be effective in noisy environments. Wavelet transform has become an essential tool for many applications, but its effectiveness is influenced by main parameters. Determination of mother wavelet function and decomposition level (DL) are important key factors to demonstrate the advantages of wavelet denoising. So, selection of optimal mother wavelet with DL is a main challenge to current algorithms. The principal aim of this study was the choice of an appropriate criterion for finding the optimal DL and the optimal mother wavelet function according to four criteria which are: signal-to-noise ratio (SNR), mean square error (MSE), percentage root-mean-square difference (PRD) and the structure similarity index measure (SSIM) for testing the robustness of the proposed algorithm. The proposed method is applied to the PCG signal contaminated with four colored noise types, in addition to the Gaussian noise. The obtained results show the effectiveness of the proposed method in reducing noise from the noisy PCG signals, especially at a low SNR.

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De Robertis, Alex, e Ian Higginbottom. "A post-processing technique to estimate the signal-to-noise ratio and remove echosounder background noise". ICES Journal of Marine Science 64, n. 6 (1 settembre 2007): 1282–91. http://dx.doi.org/10.1093/icesjms/fsm112.

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Abstract De Robertis, A., and Higginbottom, I. 2007. A post-processing technique to estimate the signal-to-noise ratio and remove echosounder background noise. – ICES Journal of Marine Science, 64: 1282–1291. A simple and effective post-processing technique to estimate echosounder background-noise levels and signal-to-noise ratios (SNRs) during active pinging is developed. Similar to other methods of noise estimation during active pinging, this method assumes that some portion of the sampled acoustic signal is dominated by background noise, with a negligible contribution from the backscattered transmit signal. If this assumption is met, the method will provide robust and accurate estimates of background noise equivalent to that measured by the receiver if the transmitter were disabled. It provides repeated noise estimates over short intervals of time without user intervention, which is beneficial in cases where background noise changes over time. In situations where background noise is dominant in a portion of the recorded signal, it is straightforward to make first-order corrections for the effects of noise and to estimate the SNR to evaluate the effects of background noise on acoustic measurements. Noise correction and signal-to-noise-based thresholds have the potential to improve inferences from acoustic measurements in lower signal-to-noise situations, such as when surveying from noisy vessels, using multifrequency techniques, surveying at longer ranges, and when working with weak acoustic targets such as invertebrates and fish lacking swimbladders.

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Shen, Chen, Yu, Ge, Han e Duan. "A Direct Current Measurement Method Based on Terbium Gallium Garnet Crystal and a Double Correlation Detection Algorithm". Sensors 19, n. 13 (7 luglio 2019): 2997. http://dx.doi.org/10.3390/s19132997.

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When applying an optical current transformer (OCT) to direct current measurement, output signals exhibit a low signal-to-noise ratio and signal-to-noise band overlap. Sinusoidal wave modulation is used to solve this problem. A double correlation detection algorithm is used to extract the direct current (DC) signal, remove white noise and improve the signal-to-noise ratio. Our sensing unit uses a terbium gallium garnet crystal in order to increase the output signal-to-noise ratio and measurement sensitivity. Measurement errors of single correlation and double correlation detection algorithms are compared, and experimental results showed that this measurement method can control measurement error to about 0.3%, thus verifying its feasibility.

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