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Journal articles on the topic 'Noise 1'

Author: Grafiati

Published: 4 June 2021

Last updated: 7 February 2022

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1

Shi, Yao-Wu, Chen Wang, Lan-Xiang Zhu, Li-Fei Deng, Yi-Ran Shi, and De-Min Wang. "1/f spectrum estimation based on α-stable distribution in colored Gaussian noise environments." Journal of Low Frequency Noise, Vibration and Active Control 38, no. 1 (December 4, 2018): 18–35. http://dx.doi.org/10.1177/1461348418813291.

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The main goal of this paper is to suppress the effect of unavoidable colored Gaussian noise on declining accuracy of transistor 1/f spectrum estimation. Transistor noises are measured by a nondestructive cross-spectrum measurement method, which is first to amplify the voltage signals through ultra-low noise amplifiers, then input the weak signals into data acquisition card. The data acquisition card collects the voltage signals and outputs the amplified noise for further analysis. According to our studies, the output 1/f noise can be characterized more accurately as non-Gaussian α-stable distribution rather than Gaussian distribution. Therefore, by utilizing the properties of α-stable distribution, we propose a cross-spectrum method effective in noisy environments based on samples normalized cross-correlation function. Simulation results and diodes output noise spectrum estimation results confirm the effectiveness of our method.

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2

Zaklikiewicz, A. M. "1/f noise of avalanche noise." Solid-State Electronics 43, no. 1 (January 1999): 11–15. http://dx.doi.org/10.1016/s0038-1101(98)00204-4.

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3

Liu, Xingchen, Qicai Zhou, Jiong Zhao, Hehong Shen, and Xiaolei Xiong. "Fault Diagnosis of Rotating Machinery under Noisy Environment Conditions Based on a 1-D Convolutional Autoencoder and 1-D Convolutional Neural Network." Sensors 19, no. 4 (February 25, 2019): 972. http://dx.doi.org/10.3390/s19040972.

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Deep learning methods have been widely used in the field of intelligent fault diagnosis due to their powerful feature learning and classification capabilities. However, it is easy to overfit depth models because of the large number of parameters brought by the multilayer-structure. As a result, the methods with excellent performance under experimental conditions may severely degrade under noisy environment conditions, which are ubiquitous in practical industrial applications. In this paper, a novel method combining a one-dimensional (1-D) denoising convolutional autoencoder (DCAE) and a 1-D convolutional neural network (CNN) is proposed to address this problem, whereby the former is used for noise reduction of raw vibration signals and the latter for fault diagnosis using the de-noised signals. The DCAE model is trained with noisy input for denoising learning. In the CNN model, a global average pooling layer, instead of fully-connected layers, is applied as a classifier to reduce the number of parameters and the risk of overfitting. In addition, randomly corrupted signals are adopted as training samples to improve the anti-noise diagnosis ability. The proposed method is validated by bearing and gearbox datasets mixed with Gaussian noise. The experimental result shows that the proposed DCAE model is effective in denoising and almost causes no loss of input information, while the using of global average pooling and input-corrupt training improves the anti-noise ability of the CNN model. As a result, the method combined the DCAE model and the CNN model can realize high-accuracy diagnosis even under noisy environment.

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EL MELLALI, TARIK, and YOUSSEF OUKNINE. "WEAK CONVERGENCE FOR QUASILINEAR STOCHASTIC HEAT EQUATION DRIVEN BY A FRACTIONAL NOISE WITH HURST PARAMETER H ∈ (½, 1)." Stochastics and Dynamics 13, no. 03 (May 27, 2013): 1250024. http://dx.doi.org/10.1142/s0219493712500244.

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In this paper, we consider a quasi-linear stochastic heat equation in one dimension on [0, 1], with Dirichlet boundary conditions driven by an additive fractional white noise. We formally replace the random perturbation by a family of noisy inputs depending on a parameter n ∈ ℕ which can approximate the fractional noise in some sense. Then, we provide sufficient conditions ensuring that the real-valued mild solution of the SPDE perturbed by this family of noises converges in law, in the space [Formula: see text] of continuous functions, to the solution of the fractional noise driven SPDE.

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5

Ward, Lawrence, and Priscilla Greenwood. "1/f noise." Scholarpedia 2, no. 12 (2007): 1537. http://dx.doi.org/10.4249/scholarpedia.1537.

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6

Wang, Chen, Yao-Wu Shi, Lan-Xiang Zhu, Li-Fei Deng, Yi-Ran Shi, and De-Min Wang. "Auto-regressive moving average parameter estimation for 1/f process under colored Gaussian noise background." Journal of Algorithms & Computational Technology 13 (January 2019): 174830261986743. http://dx.doi.org/10.1177/1748302619867439.

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Current algorithms for estimating auto-regressive moving average parameters of transistor 1/f process are usually under noiseless background. Transistor noises are measured by a non-destructive cross-spectrum measurement technique, with transistor noise first passing through dual-channel ultra-low noise amplifiers, then inputting the weak signals into data acquisition card. The data acquisition card collects the voltage signals and outputs the amplified noise for further analysis. According to our studies, the output transistor 1/f noise can be characterized more accurately as non-Gaussian α-stable distribution rather than Gaussian distribution. We define and consistently estimate the samples normalized cross-correlations of linear SαS processes, and propose a samples normalized cross-correlations-based auto-regressive moving average parameter estimation method effective in noisy environments. Simulation results of auto-regressive moving average parameter estimation exhibit good performance.

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7

Hsu, Chang Francis, Long Hsu, and Sien Chi. "Complexity and Disorder of 1/fα Noises." Entropy 22, no. 10 (October 4, 2020): 1127. http://dx.doi.org/10.3390/e22101127.

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The complexity and the disorder of a 1/fα noise time series are quantified by entropy of entropy (EoE) and average entropy (AE), respectively. The resulting EoE vs. AE plot of a series of 1/fα noises of various values of α exhibits a distinct inverted U curve. For the 1/fα noises, we have shown that α decreases monotonically as AE increases, which indicates that α is also a measure of disorder. Furthermore, a 1/fα noise and a cardiac interbeat (RR) interval series are considered equivalent as they have the same AE. Accordingly, we have found that the 1/fα noises for α around 1.5 are equivalent to the RR interval series of healthy subjects. The pink noise at α = 1 is equivalent to atrial fibrillation (AF) RR interval series while the white noise at α = 0 is more disordered than AF RR interval series. These results, based on AE, are different from the previous ones based on spectral analysis. The testing macro-average F-score is 0.93 when classifying the RR interval series of three groups using AE-based α, while it is 0.73 when using spectral-analysis-based α.

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8

Klimontovich, Yu L., and J. P. Boon. "Natural Flicker Noise (“1/ f Noise”) in Music." Europhysics Letters (EPL) 3, no. 4 (February 15, 1987): 395–99. http://dx.doi.org/10.1209/0295-5075/3/4/002.

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9

van der Ziel, A., and P. H. Handel. "Quantum 1/f noise phenomena in semiconductor noise." Physica B+C 129, no. 1-3 (March 1985): 578–79. http://dx.doi.org/10.1016/0378-4363(85)90648-5.

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10

NAKANO, Aritomo. "Noise control technologies. 1." Journal of Environmental Conservation Engineering 17, no. 7 (1988): 467–71. http://dx.doi.org/10.5956/jriet.17.467.

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11

Hooge, F. N. "1/f noise sources." IEEE Transactions on Electron Devices 41, no. 11 (1994): 1926–35. http://dx.doi.org/10.1109/16.333808.

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12

Merino-Martínez, Roberto, Eleonora Neri, Mirjam Snellen, John Kennedy, Dick G. Simons, and Gareth J. Bennett. "Multi-Approach Study of Nose Landing Gear Noise." Journal of Aircraft 57, no. 3 (May 2020): 517–33. http://dx.doi.org/10.2514/1.c035655.

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13

Makarewicz, Rufin, Piotr Kobowski, Roman Gobebiewski, and Michal Galuszka. "Transportation noise composoed of identifiable noise events." Noise Control Engineering Journal 63, no. 4 (July 1, 2015): 331–38. http://dx.doi.org/10.3397/1/376329.

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14

Carr, Daniel, and Patricia Davies. "Perception of stationary wind noise in vehicles." Noise Control Engineering Journal 69, no. 1 (January 1, 2021): 53–65. http://dx.doi.org/10.3397/1/37695.

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Predictors of people's responses to noise inside cars are used by car companies to identify and address potential noise problems from tests. Because significant improvements have been made in engine, powertrain, and tire/road noise, it is now important to pursue improvements in wind or aerodynamic noise. While models of loudness are commonly used to predict people's responses to stationary wind noise, some wind noises are less acceptable than is predicted by loudness metrics. Additional sound characteristics may account for this. Three listening studies were designed to examine the usefulness of including additional sound quality metrics with loudness in models used to predict acceptability for stationary wind-noise sounds. Test sounds were based on recordings made in cars in a wind tunnel. Signal modification techniques were developed to decorrelate metrics across a set of sounds and to examine how acceptability changes with strengths of particular sound characteristics. Models of acceptability for stationary wind noise are significantly improved when a metric that predicts the sharpness of a sound is included in the model with the loudness metric.

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15

Wang, Chen, Yao-Wu Shi, Lan-Xiang Zhu, Li-Fei Deng, Yi-Ran Shi, and De-Min Wang. "α-spectrum estimation for 1/f processes in noisy environments." Noise & Vibration Worldwide 50, no. 2 (February 2019): 46–55. http://dx.doi.org/10.1177/0957456519827937.

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In the past, 1/ f noise was regarded as a stochastic process that accords with Gaussian distribution. According to our studies, the output transistor 1/ f noise can be characterized more accurately as non-Gaussian α-stable distribution rather than Gaussian distribution. We define and consistently estimate the samples normalized cross-correlations of linear S αS processes and propose a samples normalized cross-correlations–based α-spectrum method effective in noisy environments. Simulation results and diodes noise spectrum estimation results exhibit good performance.

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16

Bennett, Gareth J., Eleonora Neri, and John Kennedy. "Noise Characterization of a Full-Scale Nose Landing Gear." Journal of Aircraft 55, no. 6 (November 2018): 2476–90. http://dx.doi.org/10.2514/1.c034750.

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17

Engel, Margret Sibylle, and Paulo Henrique Trombetta Zannin. "Noise assessment of the area of a redesigned urban expressway based on noise measurements, noise maps and noise perception interviews." Noise Control Engineering Journal 65, no. 6 (November 1, 2017): 590–610. http://dx.doi.org/10.3397/1/376572.

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18

Costanzi, Barry N., Daniel E. Endean, and E. Dan Dahlberg. "Noise in Mesoscale Magnetic Dots From Random Telegraph Noise to 1/ ${f}$ Noise." IEEE Transactions on Magnetics 55, no. 2 (February 2019): 1–3. http://dx.doi.org/10.1109/tmag.2018.2864118.

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19

Nie, Xin Hua, Zhong Ming Pan, and Wen Na Zhang. "Wavelet Based Noise Reduction for Magnetic Anomaly Signal Contaminated by 1/f Noise." Advanced Materials Research 889-890 (February 2014): 776–79. http://dx.doi.org/10.4028/www.scientific.net/amr.889-890.776.

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Magnetic anomaly detection is a passive method for detection of a ferromagnetic target, and its performance is often limited by external noise with a power spectral density of 1/fa, (0<a<2). In consideration of this kind of noise is non-stationary, self-similarity and long-range correlation, an effective noise reduction method based on the wavelet transform is proposed in this paper. The proposed method is only take one parameter into account, while the hard thresholding and soft thresholding methods utilize the relationship of the variance of the noisy signal. The simulation results show that the performance of our proposed method is superior to that of other methods.

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20

Hong, Yang, Li Ya-an, and Li Guo-Hui. "Noise reduction method of ship radiated noise with ensemble empirical mode decomposition of adaptive noise." Noise Control Engineering Journal 64, no. 2 (March 1, 2016): 230–42. http://dx.doi.org/10.3397/1/376374.

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21

Ninness, B. "Estimation of 1/f noise." IEEE Transactions on Information Theory 44, no. 1 (1998): 32–46. http://dx.doi.org/10.1109/18.650986.

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22

Lowen, S. B., and M. C. Teich. "Generalised 1/f shot noise." Electronics Letters 25, no. 16 (1989): 1072. http://dx.doi.org/10.1049/el:19890718.

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23

Spencer, R. R., and J. Grishaw. "Simplified 1/f noise calculations." Electronics Letters 27, no. 4 (1991): 312. http://dx.doi.org/10.1049/el:19910197.

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24

WEST, BRUCE J. "NETWORKS AND 1/f NOISE." Fluctuation and Noise Letters 10, no. 04 (December 2011): 515–31. http://dx.doi.org/10.1142/s0219477511000703.

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Complex networks form one of the most challenging areas of modern research overarching the traditional scientific disciplines. Of particular importance is the manner in which information is shuttled back and forth between such networks, and whether or not there exists general principles that guide the flow of information. Herein, we identify Wiener's rule, which conjectures how information is transfered in an information-dominated process. Moreover, we show that this rule is a consequence of the Principle of Complexity Management (PCM) that determines the information exchange between complex networks. A consequence of the PCM is that the maximum information transfer occurs at a 1/f noise resonance. The information transfer between two complex networks is also determined by direct numerical calculation of a master equation model of network dynamics using interacting two-state elements, the decision-making model (DMM). The DMM generates phase transitions and on a two-dimensional lattice, reduces to the Ising model in an appropriate limit. The computations using the DMM suggest that the inverse power laws of links and survival probability are not necessarily related.

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25

Degerli, Y., F. Lavernhe, P. Magnan, and J. Farré. "Bandlimited 1/f-noise source." Electronics Letters 35, no. 7 (1999): 521. http://dx.doi.org/10.1049/el:19990417.

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26

van der Ziel, A. "Unified presentation of 1/f noise in electron devices: fundamental 1/f noise sources." Proceedings of the IEEE 76, no. 3 (March 1988): 233–58. http://dx.doi.org/10.1109/5.4401.

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27

Petety, Aditya, Sandhya Tripathi, and N. Hemachandra. "Attribute Noise Robust Binary Classification (Student Abstract)." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 10 (April 3, 2020): 13897–98. http://dx.doi.org/10.1609/aaai.v34i10.7221.

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We consider the problem of learning linear classifiers when both features and labels are binary. In addition, the features are noisy, i.e., they could be flipped with an unknown probability. In Sy-De attribute noise model, where all features could be noisy together with same probability, we show that 0-1 loss (l0−1) need not be robust but a popular surrogate, squared loss (lsq) is. In Asy-In attribute noise model, we prove that l0−1 is robust for any distribution over 2 dimensional feature space. However, due to computational intractability of l0−1, we resort to lsq and observe that it need not be Asy-In noise robust. Our empirical results support Sy-De robustness of squared loss for low to moderate noise rates.

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28

Du, Lei, Yiqi Zhuang, and Yong Wu. "1/fγ Noise separated from white noise with wavelet denoising." Microelectronics Reliability 42, no. 2 (February 2002): 183–88. http://dx.doi.org/10.1016/s0026-2714(01)00249-9.

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29

Salomons, Erik M., and Frank H. A. van den Berg. "Measurements and computations of shooting noise. Part 1: Muzzle noise." Journal of the Acoustical Society of America 105, no. 2 (February 1999): 1133. http://dx.doi.org/10.1121/1.425397.

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30

Lawrence, A. F., G. G. Liao, and A. T. Abawi. "Noise and Nonlinearities in (1 + 1)-Dimensional System." Communications in Theoretical Physics 16, no. 2 (September 1991): 195–200. http://dx.doi.org/10.1088/0253-6102/16/2/195.

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31

Staiano, Michael A. "Influence of pavement type and aggregate size on tire-pavement noise generation." Noise Control Engineering Journal 69, no. 2 (March 1, 2021): 162–72. http://dx.doi.org/10.3397/1/376916.

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Among the sources of vehicle noise, the interaction of tires with the pavement is the most important. Tire-pavement noise is the result of a number of generation and amplification mechanisms as the tire rolls along the pavement. These mechanisms tend to fall into independent low-frequency and high-frequency ranges. In this current study, 24 measured pavements were grouped by type and evaluated via multiple linear regression analyses with respect to vehicle speed and specified aggregate dimensions. The evaluation found that tire-pavement noise variation for a specific pavement type is explained largely by aggregate size. Tire-pavement noise tended to increase with aggregate size—a behavior consistently exhibited, for example, by SMA pavements. Porous asphalt pavements ranged from relatively quiet to relatively noisy depending upon aggregate size. The ultimate goal of this work is the development of methods enabling the design of quieter pavements using analytical means.

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32

Gabard, G. "Noise Sources for Duct Acoustics Simulations: Broadband Noise and Tones." AIAA Journal 52, no. 9 (September 2014): 1994–2006. http://dx.doi.org/10.2514/1.j052739.

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33

Bertsch, Lothar, Gertjan Looye, Eckhard Anton, and Stefan Schwanke. "Flyover Noise Measurements of a Spiraling Noise Abatement Approach Procedure." Journal of Aircraft 48, no. 2 (March 2011): 436–48. http://dx.doi.org/10.2514/1.c001005.

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34

Choi, Wongyu, Michael B. Pate, and James F. Sweeney. "Uncertainty and Signal-to-Noise Ratio for Unsteady Background Noise." Noise Control Engineering Journal 66, no. 2 (April 1, 2018): 131–41. http://dx.doi.org/10.3397/1/376612.

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35

Liu, Ningning, Yuedong Sun, Yansong Wang, Hui Guo, Bin Gao, Tianpei Feng, and Pei Sun. "Active control for vehicle interior noise using the improved iterative variable step-size and variable tap-length LMS algorithms." Noise Control Engineering Journal 67, no. 6 (November 1, 2019): 405–14. http://dx.doi.org/10.3397/1/376737.

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Active noise control (ANC) is used to reduce undesirable noise, particularly at low frequencies. There are many algorithms based on the least mean square (LMS) algorithm, such as the filtered-x LMS (FxLMS) algorithm, which have been widely used for ANC systems. However, the LMS algorithm cannot balance convergence speed and steady-state error due to the fixed step size and tap length. Accordingly, in this article, two improved LMS algorithms, namely, the iterative variable step-size LMS (IVS-LMS) and the variable tap-length LMS (VT-LMS), are proposed for active vehicle interior noise control. The interior noises of a sample vehicle are measured and thereby their frequency characteristics. Results show that the sound energy of noise is concentrated within a low-frequency range below 1000 Hz. The classical LMS, IVS-LMS and VT-LMS algorithms are applied to the measured noise signals. Results further suggest that the IVS-LMS and VT-LMS algorithms can better improve algorithmic performance for convergence speed and steady-state error compared with the classical LMS. The proposed algorithms could potentially be incorporated into other LMS-based algorithms (like the FxLMS) used in ANC systems for improving the ride comfort of a vehicle.

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36

HANDEL, PETER H., and ADAM G. TOURNIER. "QUANTUM 1/f NOISE AND QUANTUM 1/f PHASE NOISE RELATED TO THE UNCERTAINTY RELATIONS." International Journal of Modern Physics B 20, no. 11n13 (May 20, 2006): 1621–28. http://dx.doi.org/10.1142/s0217979206033887.

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Quantum 1/f noise is the manifestation of the coherent and conventional quantum 1/f effects (Q1/fE). The conventional Q1/fE is a fundamental quantum fluctuation of physical cross sections σ and process rates Γ, caused by the bremsstrahlung (recoil) energy and momentum losses of charged particles, when they are scattered, or accelerated in any way. The closely related coherent Q1/fE is present in any current carried by many particles. It is caused by the energy spread characterizing any coherent state of the electromagnetic field oscillators. According to the Heisenberg's uncertainty principle, because an approximation of the phase or position variable is known, exact knowledge of the energy is precluded. This energy spread results in nonstationary energy values, or fluctuations in the energy of the oscillators. To find the spectral density of these inescapable basic fluctuations, which are known to characterize any quantum state, which is not an energy eigenstate, we use an elementary physical derivation based on Schrödinger's definition of coherent states, which can be supplemented by a rigorous derivation from a well-known quantum-electrodynamical branch-point propagator. The example of a simple harmonic oscillator is also useful for illustrating the uncertainty that arises due to Q 1/f Noise. Clearly illustrating the relation between the uncertainty principle and Q 1/f noise.

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Jules Étienne, Cédric, Angelo Arleo, and Rémy Allard. "Maximizing noise energy for noise-masking studies." Behavior Research Methods 49, no. 4 (September 7, 2016): 1278–90. http://dx.doi.org/10.3758/s13428-016-0786-1.

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38

Siriopoulos, Costas, and Alexandros Leontitsis. "Nonlinear Noise Estimation in International Capital Markets." Multinational Finance Journal 6, no. 1 (March 1, 2002): 43–63. http://dx.doi.org/10.17578/6-1-3.

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39

Lee, Nokhaeng, and Youngjin Park. "Active Noise Control for Dishwasher noise." Journal of Physics: Conference Series 744 (September 2016): 012189. http://dx.doi.org/10.1088/1742-6596/744/1/012189.

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Yang, Wonyoung, Myung-Jun Kim, and Hyeun Jun Moon. "Effects of indoor temperature and background noise on floor impact noise perception." Indoor and Built Environment 28, no. 4 (January 23, 2018): 454–69. http://dx.doi.org/10.1177/1420326x17753708.

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This study investigates effects of room air temperature and background noise on the perception of floor impact noises in a room. Floor impact noises were recorded in apartment buildings and were presented in an indoor climate chamber with background noise for subjective evaluation. Thirty-two participants were subjected to all combinations of three thermal conditions (20%C, 25%C, 30%C and relative humidity 50%), four background noise types (Babble, Fan, Traffic and Water), three background noise levels (35 dBA, 40 dBA and 45 dBA) and four floor impact noises (Man Jumping, Children Running, Man Running and Chair Scraping). After a 1-h thermal adaptation period for each thermal condition, the participants were asked to evaluate their thermal and acoustic perceptions. Statistically significant effects were found for the room air temperature and background noise level on the perception of the floor impact noises. Noisiness, loudness and complaints of floor impact noise increased with increasing room temperature and background noise level. Annoyance of floor impact noise showed a peak in acceptable thermal environment for general comfort. Room air temperature was a dominant non-auditory factor contributing to floor impact noise annoyance, while the floor impact noise level influenced the floor impact noise loudness and the floor impact noisiness was almost equally affected by the room temperature, background noise level and floor impact noise level. Further investigation is needed to fully understand the combined perception of floor impact noise under various indoor environmental conditions.

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41

HALLEY, JOHN M., and PABLO INCHAUSTI. "THE INCREASING IMPORTANCE OF 1/f-NOISES AS MODELS OF ECOLOGICAL VARIABILITY." Fluctuation and Noise Letters 04, no. 02 (June 2004): R1—R26. http://dx.doi.org/10.1142/s0219477504001884.

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The features of 1/f-noise processes offer important new insights into the field of population biology, greatly helping our quest for understanding and for prediction of ecological processes. 1/f-noises account quite satisfactorily for the observed nature of ecological fluctuations. This article reviews the application of 1/f-noise processes to ecology. After a discussion of the basic problems of population ecology that makes such an innovation necessary, we review the features of 1/f-noises concentrating especially on those aspects that make these processes attractive as a solution. We also present a discussion of the analysis of real ecological data, which confirms that there are good empirical as well as good theoretical reasons to establish a leading role for pink 1/f noise. We then discuss the consequences of such a model for our understanding of ecology. The article finishes with a number of observations about some aspects of ecological data and applications that are likely to drive research in a different direction from that associated with engineering and the physical sciences.

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42

Im, Ki-Sik, Mallem Siva Pratap Reddy, Yeo Jin Choi, Youngmin Hwang, Sung Jin An, and Jea-Seung Roh. "Investigation of 1/f and Lorentzian Noise in TMAH-treated Normally-Off GaN MISFETs." Crystals 10, no. 8 (August 18, 2020): 717. http://dx.doi.org/10.3390/cryst10080717.

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A tetramethyl ammonium hydroxide (TMAH)-treated normally-off Gallum nitride (GaN) metal-insulator-semiconductor field-effect transistor (MISFET) was fabricated and characterized using low-frequency noise (LFN) measurements in order to find the conduction mechanism and analyze the trapping behavior into the gate insulator as well as the GaN buffer layer. At the on-state, the noise spectra in the fabricated GaN device were 1/fγ properties with γ ≈ 1, which is explained by correlated mobility fluctuations (CMF). On the other hand, the device exhibited Lorentzian or generation-recombination (g-r) noises at the off-state due to deep-level trapping/de-trapping into the GaN buffer layer. The trap time constants (τi) calculated from the g-r noises became longer when the drain voltage increased up to 5 V, which was attributed to deep-level traps rather than shallow traps. The severe drain lag was also investigated from pulsed I-V measurement, which is supported by the noise behavior observed at the off-state.

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43

KIMELBLAT, VLADIMIR. "APPLYING 1/F NOISE FOR DRIFT ESTIMATION." Fluctuation and Noise Letters 10, no. 02 (June 2011): 181–88. http://dx.doi.org/10.1142/s021947751100051x.

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The work introduces a new 1/f noise theory that focuses on limited signals. Usually, 1/f noise represents drift, because 1/f noise is the spectral power density of the drift. The subjects of the new theory are signals that have limited value and duration. Therefore the basis of the new theory of 1/f noise corresponds to the real properties of any device and signal comprising 1/f noise. On this basis, the standard deviation of 1/f noise was derived, which is the most important parameter of 1/f noise. This standard deviation has good consistency with (a) the widely used Hurst approximation, (b) the square root of time dependence of Brownian motion displacement, and (c) theoretically derived Brownian displacement. Comparison of an existing 1/f noise theory and the new one shows that both theories are incompatible because subjects of the existing 1/f noise theory are limitless signals, which do not exist.

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44

Rust, Ryan L., Scott D. Sommerfeldt, Kent L. Gee, and Jonathan D. Blotter. "Characterization of microphone placement and noise sensitivity in a global active noise control system for a compact noise source." Noise Control Engineering Journal 61, no. 3 (May 1, 2013): 280–90. http://dx.doi.org/10.3397/1/3761024.

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45

Kendal, Wayne S. "Fluctuation Scaling and 1/f Noise." Journal of Basic and Applied Physics 2, no. 2 (May 8, 2013): 40–49. http://dx.doi.org/10.5963/jbap0202002.

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KISHIMOTO, KENJI. "1. NOISE PROPERTIES OF DIGITAL RADIOGRAPHY." Japanese Journal of Radiological Technology 47, no. 6 (1991): 854–58. http://dx.doi.org/10.6009/jjrt.kj00003323650.

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Kazakov, Kirill A. "1/f noise and quantum indeterminacy." Physics Letters A 384, no. 31 (November 2020): 126812. http://dx.doi.org/10.1016/j.physleta.2020.126812.

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Dewey, T. G., and J. G. Bann. "Protein dynamics and 1/f noise." Biophysical Journal 63, no. 2 (August 1992): 594–98. http://dx.doi.org/10.1016/s0006-3495(92)81603-x.

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Kaulakys, B. "Autoregressive model of 1/f noise." Physics Letters A 257, no. 1-2 (June 1999): 37–42. http://dx.doi.org/10.1016/s0375-9601(99)00284-4.

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Tacano, M., and Y. Sugiyama. "1/f noise in GaAs filaments." IEEE Transactions on Electron Devices 38, no. 11 (1991): 2548–53. http://dx.doi.org/10.1109/16.97421.

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