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Journal articles on the topic 'Intrinsic noise'

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Author: Grafiati

Published: 4 June 2021

Last updated: 11 February 2022

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1

Yang, Ren Di, and Yan Li Zhang. "Denoising of ECG Signal Based on Empirical Mode Decomposition and Adaptive Noise Cancellation." Applied Mechanics and Materials 40-41 (November 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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2

Sun, Mengyi, and Jianzhi Zhang. "Allele-specific single-cell RNA sequencing reveals different architectures of intrinsic and extrinsic gene expression noises." Nucleic Acids Research 48, no. 2 (December 4, 2019): 533–47. http://dx.doi.org/10.1093/nar/gkz1134.

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Abstract Gene expression noise refers to the variation of the expression level of a gene among isogenic cells in the same environment, and has two sources: extrinsic noise arising from the disparity of the cell state and intrinsic noise arising from the stochastic process of gene expression in the same cell state. Due to the low throughput of the existing method for measuring the two noise components, the architectures of intrinsic and extrinsic expression noises remain elusive. Using allele-specific single-cell RNA sequencing, we here estimate the two noise components of 3975 genes in mouse fibroblast cells. Our analyses verify predicted influences of several factors such as the TATA-box and microRNA targeting on intrinsic or extrinsic noises and reveal gene function-associated noise trends implicating the action of natural selection. These findings unravel differential regulations, optimizations, and biological consequences of intrinsic and extrinsic noises and can aid the construction of desired synthetic circuits.

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3

XIE, ZHI, and DON KULASIRI. "ON EXPLORING EFFECTS OF MOLECULAR NOISE IN A SIMPLE VIRAL INFECTION MODEL." International Journal of Biomathematics 03, no. 01 (March 2010): 1–19. http://dx.doi.org/10.1142/s1793524510000891.

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Intrinsic and extrinsic noises are all believed to be important in the development and function of many living organisms. In this study, we investigate the sources of the intrinsic noise and the influence of the extrinsic noise on an intracellular viral infection system. The contribution of the intrinsic noise from each reaction is measured by means of a special form of stochastic differential equations (SDEs), chemical Langevin equation. The intrinsic noise of the system is a linear sum of the noise in each of the reactions. The intrinsic noise mainly arises from the degradation of mRNA and the transcription processes. We then study the effects of extrinsic noise by the means of a general form of SDE. It is found that the noise of the viral components grows logarithmically with the increasing noise intensities. The system is most susceptible to the noise in the virus assembly process. A high level of noise in this process can even inhibit the growth of the viruses. This study also demonstrates the utility of SDEs in analyzing genetic regulatory networks perturbed by either inherent or parametric stochasticity.

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4

Liu, Shengjun, Qi Wang, and Hai Feng. "The correlation between intrinsic noise and extrinsic noise." Physica A: Statistical Mechanics and its Applications 392, no. 20 (October 2013): 5138–42. http://dx.doi.org/10.1016/j.physa.2013.06.032.

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5

Hayden, David, Ye Yuan, and Jorge Goncalves. "Network Identifiability from Intrinsic Noise." IEEE Transactions on Automatic Control 62, no. 8 (August 2017): 3717–28. http://dx.doi.org/10.1109/tac.2016.2640219.

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6

KHALDI, KAIS, MONIA TURKI-HADJ ALOUANE, and ABDEL-OUAHAB BOUDRAA. "VOICED SPEECH ENHANCEMENT BASED ON ADAPTIVE FILTERING OF SELECTED INTRINSIC MODE FUNCTIONS." Advances in Adaptive Data Analysis 02, no. 01 (January 2010): 65–80. http://dx.doi.org/10.1142/s1793536910000409.

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In this paper a new method for voiced speech enhancement combining the Empirical Mode Decomposition (EMD) and the Adaptive Center Weighted Average (ACWA) filter is introduced. Noisy signal is decomposed adaptively into intrinsic oscillatory components called Intrinsic Mode Functions (IMFs). Since voiced speech structure is mostly distributed on both medium and low frequencies, the shorter scale IMFs of the noisy signal are beneath noise, however the longer scale ones are less noisy. Therefore, the main idea of the proposed approach is to only filter the shorter scale IMFs, and to keep the longer scale ones unchanged. In fact, the filtering of longer scale IMFs will introduce distortion rather than reducing noise. The denoising method is applied to several voiced speech signals with different noise levels and the results are compared with wavelet approach, ACWA filter and EMD–ACWA (filtering of all IMFs using ACWA filter). Relying on exhaustive simulations, we show the efficiency of the proposed method for reducing noise and its superiority over other denoising methods, i.e. to improve Signal-to-Noise Ratio (SNR), and to offer better listening quality based on a Perceptual Evaluation of Speech Quality (PESQ). The present study is limited to signals corrupted by additive white Gaussian noise.

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7

Shen, Tao, Zhangcai Long, and Bo Chen. "External noise suppression by intrinsic noise in a neuron." Results in Physics 15 (December 2019): 102615. http://dx.doi.org/10.1016/j.rinp.2019.102615.

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8

VÁZQUEZ-JIMÉNEZ, AARÓN, MOISÉS SANTILLÁN, and JESÚS RODRÍGUEZ-GONZÁLEZ. "CHARACTERIZATION OF INTRINSIC AND EXTRINSIC NOISE EFFECTS IN POSITIVELY REGULATED GENES." Journal of Biological Systems 27, no. 03 (September 2019): 383–98. http://dx.doi.org/10.1142/s0218339019500165.

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Gene regulation is fundamental for cell survival. This regulation must be both robust to noise and sensitive enough to external stimuli to elicit the proper responses. In this work, we study, through stochastic numerical simulations, how a gene regulatory network with a positive feedback loop responds to environmental changes in the presence of intrinsic and extrinsic noises. Noise effects were characterized by measuring the statistical differences between two protein time series resulting from identical systems subject to the same source of extrinsic noise. A robust analysis was implemented by modifying the kinetic system parameters. We found that the common source of time-varying extrinsic fluctuations leads to a correlation in the systems it affects. The correlation and the extrinsic and intrinsic noise components are modulated by the update period and noise intensity parameters. Our results suggest that noise perception is controlled through the parameters associated with the response time: degradation rates and promoter dissociation constant.

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9

Szasz, Oliver, Gyula Peter Szigeti, and Andras Szasz. "Intrinsic Noise Monitoring of Complex Systems." Open Journal of Biophysics 07, no. 04 (2017): 197–215. http://dx.doi.org/10.4236/ojbiphy.2017.74015.

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10

Prebianca, Flavio, Holokx A. Albuquerque, and Marcus W. Beims. "Describing intrinsic noise in Chua's circuit." Physics Letters A 382, no. 35 (September 2018): 2420–23. http://dx.doi.org/10.1016/j.physleta.2018.05.054.

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11

Thattai, M., and A. van Oudenaarden. "Intrinsic noise in gene regulatory networks." Proceedings of the National Academy of Sciences 98, no. 15 (July 3, 2001): 8614–19. http://dx.doi.org/10.1073/pnas.151588598.

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12

Shankar, Prabhat, Masatoshi Nishikawa, and Tatsuo Shibata. "Adaptive Responses Limited by Intrinsic Noise." PLOS ONE 10, no. 8 (August 25, 2015): e0136095. http://dx.doi.org/10.1371/journal.pone.0136095.

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13

LI, QIAN SHU, and AI ZHONG LEI. "INTRINSIC FLUCTUATION OF CHUA SYSTEM." International Journal of Bifurcation and Chaos 14, no. 09 (September 2004): 3269–75. http://dx.doi.org/10.1142/s021812740401117x.

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Intrinsic fluctuation of Chua system is studied with master equation method. Our results have shown that the intrinsic noise indeed exerted considerable influence on Chua system. In contrast to that of deterministic equation the patterns of time evolution and attractor have been greatly altered under the influence of intrinsic noise.

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14

Zhan, Liwei, Fang Ma, ZhengHui Li, HaiTao Li, and Chengwei Li. "Study on the cage slip of rolling bearing using a non-contact method." Structural Health Monitoring 19, no. 6 (May 24, 2020): 2107–21. http://dx.doi.org/10.1177/1475921720916227.

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In this article, a novel, non-contact method is proposed to detect cage slip of rolling bearing. The proposed method can obtain the speed signal of cage and inner raceway by the introduction of weak magnetic field detection technology simultaneously. Meanwhile, to enhance the identified accuracy of cage slip, an adaptive filtering method is developed to suppress noise in weak magnetic field sensor output signal. The method adopts complementary complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) to decompose the weak magnetic field sensor output signal into a set of intrinsic mode functions adaptively. The intrinsic mode functions contain noisy component of detection signal (noisy intrinsic mode functions) as well as corresponding signal component of the roller and inner raceway (signal intrinsic mode functions). The noisy- and signal intrinsic mode functions are identified by the development of reconstruction criterion. The criterion is determined based on upper boundary of rotational frequency of roller under pure rolling conditions. Then, the noisy intrinsic mode functions are forced to zero, and signal intrinsic mode functions are reconstructed to extract the speed information of roller and inner raceway. An experiment system is developed to verify the effectiveness of proposed method. The experiment result shows that the cage slip decreases with the increment in load under the given speed conditions and increases with the increment in speed under the given load conditions, as well as the cage slip is more serious under given no-load conditions. It shows that the weak magnetic field detection technology can be used to detect the cage slip of rolling bearing.

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15

Bathaei, F. Z., and J. C. Anderson. "Electrical noise measurements in intrinsic amorphous silicon." Philosophical Magazine B 55, no. 1 (January 1987): 87–100. http://dx.doi.org/10.1080/13642818708211259.

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16

Hung, Yao-Chen, and Chai-Yu Lin. "Modeling intrinsic noise in random Boolean networks." Physica A: Statistical Mechanics and its Applications 395 (February 2014): 121–27. http://dx.doi.org/10.1016/j.physa.2013.10.049.

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17

Grima, R. "Intrinsic biochemical noise in crowded intracellular conditions." Journal of Chemical Physics 132, no. 18 (May 14, 2010): 185102. http://dx.doi.org/10.1063/1.3427244.

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18

Leissing, Thomas, Jérôme Defrance, Philippe Jean, Catherine Guigou-Carter, and Jean-Pierre Clairbois. "Optimisation of noise reducing device intrinsic performances." Journal of the Acoustical Society of America 131, no. 4 (April 2012): 3264. http://dx.doi.org/10.1121/1.4708197.

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19

Scott, Matthew, Francis J. Poulin, and Herbert Tang. "Approximating intrinsic noise in continuous multispecies models." Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 467, no. 2127 (September 2010): 718–37. http://dx.doi.org/10.1098/rspa.2010.0275.

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In small-scale chemical reaction networks, the local density of molecules is changed by discrete jumps owing to reactive collisions, and through transport. A systematic perturbation scheme is developed to analytically characterize these non-equilibrium intrinsic fluctuations in a multispecies spatially varying system. The method is illustrated on a variety of model systems. In all cases, the continuous approximation method is corroborated with extensive stochastic simulation. As an example of our technique applied to a spatially varying steady state, we demonstrate that a model for embryonic patterning mediated by regulatory mRNA is surprisingly robust to intrinsic fluctuations.

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20

Güémez, J., and M. A. Matías. "Intrinsic-noise-induced transitions in chaotic systems." Physical Review E 51, no. 4 (April 1, 1995): 3059–68. http://dx.doi.org/10.1103/physreve.51.3059.

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21

Korotkov, A. N. "Intrinsic noise of the single-electron transistor." Physical Review B 49, no. 15 (April 15, 1994): 10381–92. http://dx.doi.org/10.1103/physrevb.49.10381.

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22

Torres, J. J., J. Marro, and J. F. Mejias. "Can intrinsic noise induce various resonant peaks?" New Journal of Physics 13, no. 5 (May 9, 2011): 053014. http://dx.doi.org/10.1088/1367-2630/13/5/053014.

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23

Zeng, Chunhua, Tao Yang, Qinglin Han, Chun Zhang, Dong Tian, and Hua Wang. "Noises-induced toggle switch and stability in a gene regulation network." International Journal of Modern Physics B 28, no. 31 (December 8, 2014): 1450223. http://dx.doi.org/10.1142/s0217979214502233.

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It is well-known that noises are inevitable in gene regulatory networks due to the low-copy numbers of molecules and environmental fluctuations. In this paper, we investigate the stationary probability distribution (SPD) between both low (OFF state) and high (ON state) protein levels and mean first passage time (MFPT) in an abstract model of the Myc/E2F/miR-17-92 network presented by Aguda et al., PNAS 105, 19678 (2008), where the gene expression is assumed to be disturbed simultaneously by intrinsic and extrinsic noises that were correlated. Our results show that (i) the OFF state is enhanced by the extrinsic noise (D), while the ON state is enhanced by the intrinsic noise (Q) or cross-correlation between two noises (λ); (ii) for the cases of negative or no cross-correlation (λ⩽0.0), the increase of the noise intensity (D or Q) leads to a decline of the MFPT and enhances the probability of toggle switch to the OFF state; (iii) but for the case of positive cross-correlation (λ>0.0), the MFPT as a function of the noise intensity (D or Q) exhibits a maximum, this maximum for MFPT identifies the characteristic of noise enhanced stability of the ON state and (iv) the cross-correlation between two noises can enhance stability of the ON state.

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24

Hasegawa, Masaru, Emi Arai, and Masahiko Nakamura. "Nestling, but not adult male, barn swallows emit short calls in noisy environments." Biological Journal of the Linnean Society 133, no. 1 (March 20, 2021): 57–67. http://dx.doi.org/10.1093/biolinnean/blab007.

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Abstract Some, but not all, animals cope with anthropogenic noise by changing the structure of their acoustic signals, possibly due to two interrelated factors: the design of the acoustic signals and the intrinsic biological state. Here, using the barn swallow (Hirundo rustica) that breed solitarily along streets, we studied male enticement calls and nestling food-begging calls in relation to ambient noise. The former calls are structurally quite similar to the latter, perhaps functioning as a sensory trap to exploit female parental care for nestlings. This provides a unique opportunity to study whether intrinsic biological state (i.e. developmental stage, here), virtually independent of acoustic design, matters in acoustic adaptation to anthropogenic noise. We found that the syllable length of nestling food-begging calls, but not of male enticement calls, decreased with increasing local noise levels within populations. The syllable lengths of the father’s enticement calls and those of the nestling’s food-begging calls were positively related, which explains why male enticement calls as well as nestling food-begging calls had shorter syllables in a noisy urban population compared to those in a quiet rural population. The current findings indicate that the intrinsic biological state of two acoustically similar sounds explains their differential relationships with ambient noise.

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25

WANG, LI, YUBING GONG, and XIU LIN. "ENHANCEMENT OF INTRINSIC SPIKING COHERENCE BY EXTERNAL NON-GAUSSIAN NOISE IN A STOCHASTIC HODGKIN–HUXLEY NEURON." Fluctuation and Noise Letters 10, no. 04 (December 2011): 359–69. http://dx.doi.org/10.1142/s0219477511000624.

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In this paper, we study the effect of external non-Gaussian noise on the temporal coherence of the intrinsic spiking induced by the channel noise in a stochastic Hodgkin–Huxley neuron. It is found that, for a sufficiently large membrane patch, the intrinsic spiking coherence can be enhanced by the proper values of non-Gaussian noise's strength, correlation time, or deviation from Gaussian distribution. And that the intrinsic spiking can exhibit coherence resonance when the noise's strength is optimal. This implies that the channel noise-induced intrinsic spiking may become more or the most ordered in time with the assistance of the external non-Gaussian noise. These results show that the external non-Gaussian noise can play a constructive role for improving the time precision of information processing in stochastic neurons.

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26

Pelli, Denis G. "Visual limitations to image interpretation." Proceedings, annual meeting, Electron Microscopy Society of America 45 (August 1987): 18–19. http://dx.doi.org/10.1017/s0424820100125154.

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Most images are interpreted solely by visual perception. What we can perceive in an image is limited by the quality of the image (e.g. the contrasts of the signal and noise) and by intrinsic limits of our visual systems. In this abstract I will describe the intrinsic noise in the visual system. In my presentation I will also demonstrate some limits to the complexity of patterns that may be perceived.THE EQUIVALENT INPUT NOISE OF HUMAN VISIONIn order to see, we must transduce the photons entering our eye into neural activity. The absorption of light by matter is a stochastic phenomenon. The intensity of the light determines only the probability of a photon absorption by a photoreceptor. Thus vision is intrinsically noisy, based as it is on the random absorption of photons.

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27

LONG, S., L. ESCOTTE, J. GRAFFEUIL, P. FELLON, D. GEIGER, and D. PONS. "HIGH-FREQUENCY NOISE IN PSEUDOMORPHIC DOUBLE-HETEROJUNCTION HIGH ELECTRON MOBILITY TRANSISTORS." Fluctuation and Noise Letters 02, no. 01 (March 2002): L13—L19. http://dx.doi.org/10.1142/s0219477502000531.

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The noise behavior of pseudomorphic double-heterojunction high electron mobility transistors dedicated to power applications is investigated in this paper and compared to conventional low noise field effect transistors. The noise is analyzed from an extrinsic and an intrinsic point of view. It appears that the minimum noise figure is similar for the two devices even if the intrinsic noise sources are different. We explain this phenomenon using the double-heterojunction mode of operation.

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28

González, Jorge A., Leonardo Trujillo, and Ananı́as Escalante. "Intrinsic chaos and external noise in population dynamics." Physica A: Statistical Mechanics and its Applications 324, no. 3-4 (June 2003): 723–32. http://dx.doi.org/10.1016/s0378-4371(03)00075-x.

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29

Seidel, G. M., and I. S. Beloborodov. "Intrinsic excess noise in a transition edge sensor." Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 520, no. 1-3 (March 2004): 325–28. http://dx.doi.org/10.1016/j.nima.2003.11.258.

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30

Ding, L., S. Saez, C. Dolabdjian, P. Ciureanu, L. G. C. Melo, A. Yelon, and D. Ménard. "Intrinsic Giant Magnetoimpedance Noise Reduction by DC Bias." Sensor Letters 5, no. 1 (March 1, 2007): 176–79. http://dx.doi.org/10.1166/sl.2007.052.

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31

Pu, Ye, and Hui Meng. "Intrinsic speckle noise in off-axis particle holography." Journal of the Optical Society of America A 21, no. 7 (July 1, 2004): 1221. http://dx.doi.org/10.1364/josaa.21.001221.

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32

Yu, Shumin, Yumei Wen, Ping Li, and Yao Wang. "Intrinsic noise in magnetic film/planar coil sensors." Journal of Magnetism and Magnetic Materials 518 (January 2021): 167381. http://dx.doi.org/10.1016/j.jmmm.2020.167381.

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33

Kaulakys, B. "On the intrinsic origin of 1/f noise." Microelectronics Reliability 40, no. 11 (November 2000): 1787–90. http://dx.doi.org/10.1016/s0026-2714(00)00055-x.

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34

Agassi, D. "Intrinsic 1/f noise in a superconductor strip." Physica C: Superconductivity 371, no. 1 (June 2002): 34–44. http://dx.doi.org/10.1016/s0921-4534(01)01199-6.

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35

Saito, A., K. Hamasaki, A. Irie, and G. Oya. "1/f noise of Bi2Sr2CaCu2Oy intrinsic Josephson junctions." Journal of Applied Physics 90, no. 6 (September 15, 2001): 2911–14. http://dx.doi.org/10.1063/1.1381048.

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36

Meng, Hui, W. L. Anderson, Fazle Hussain, and David D. Liu. "Intrinsic speckle noise in in-line particle holography." Journal of the Optical Society of America A 10, no. 9 (September 1, 1993): 2046. http://dx.doi.org/10.1364/josaa.10.002046.

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37

Long, Yan, Wang Hong-Li, and Ouyang Qi. "Deterministic Characterization of Intrinsic Noise in Chemical Reactions." Chinese Physics Letters 27, no. 1 (January 2010): 010501. http://dx.doi.org/10.1088/0256-307x/27/1/010501.

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38

Ocali, Ogan, and Ergin Atalar. "Ultimate intrinsic signal-to-noise ratio in MRI." Magnetic Resonance in Medicine 39, no. 3 (March 1998): 462–73. http://dx.doi.org/10.1002/mrm.1910390317.

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39

Frigola, David, Laura Casanellas, José M. Sancho, and Marta Ibañes. "Asymmetric Stochastic Switching Driven by Intrinsic Molecular Noise." PLoS ONE 7, no. 2 (February 21, 2012): e31407. http://dx.doi.org/10.1371/journal.pone.0031407.

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40

Delgado, F., K. Lopez, R. Ferreira, and J. Fernández-Rossier. "Intrinsic spin noise in MgO magnetic tunnel junctions." Applied Physics Letters 102, no. 6 (February 11, 2013): 063102. http://dx.doi.org/10.1063/1.4791594.

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41

Warmflash, A., and A. R. Dinner. "Signatures of combinatorial regulation in intrinsic biological noise." Proceedings of the National Academy of Sciences 105, no. 45 (November 3, 2008): 17262–67. http://dx.doi.org/10.1073/pnas.0809314105.

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42

Albareda, G., D. Jiménez, and X. Oriols. "Intrinsic noise in aggressively scaled field-effect transistors." Journal of Statistical Mechanics: Theory and Experiment 2009, no. 01 (January 7, 2009): P01044. http://dx.doi.org/10.1088/1742-5468/2009/01/p01044.

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43

Brown, Angela M. "Intrinsic contrast noise and infant visual contrast discrimination." Vision Research 34, no. 15 (August 1994): 1947–64. http://dx.doi.org/10.1016/0042-6989(94)90025-6.

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44

Perkins, Edmon, Masayuki Kimura, Takashi Hikihara, and Balakumar Balachandran. "Effects of noise on symmetric intrinsic localized modes." Nonlinear Dynamics 85, no. 1 (March 4, 2016): 333–41. http://dx.doi.org/10.1007/s11071-016-2688-2.

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45

Zhao, Kun, Jisheng Ding, YanFei Sun, and ZhiYuan Hu. "Side-scan Sonar Image De-noising Based on Bidimensional Empirical Mode Decomposition and Non-local Means." E3S Web of Conferences 206 (2020): 03019. http://dx.doi.org/10.1051/e3sconf/202020603019.

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In order to suppress the multiplicative specular noise in side-scan sonar images, a denoising method combining bidimensional empirical mode decomposition and non-local means algorithm is proposed. First, the sonar image is decomposed into intrinsic mode functions(IMF) and residual component, then the high frequency IMF is denoised by non-local mean filtering method, and finally the processed intrinsic mode functions and residual component are reconstructed to obtain the de-noised side-scan sonar image. The paper’s method is compared with the conventional filtering algorithm for experimental quantitative analysis. The results show that this method can suppress the sonar image noise and retain the detailed information of the image, which is beneficial to the later image processing.

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46

Page, S. A. "Noise factors for parallel plate ionization chambers." Canadian Journal of Physics 66, no. 6 (June 1, 1988): 527–29. http://dx.doi.org/10.1139/p88-087.

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Sources of intrinsic detector noise for ionization chambers operated in dc mode, suitable for parity violation measurements in proton–proton scattering at intermediate and high energy are discussed. Measurements of the intrinsic noise factor for axial and transverse-field parallel plate ionization chambers used in high-precision proton beam current measurements at TRIUMF are reported.

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47

Wang, Guang Bin, and Liang Pei Huang. "An Improved Noise Reduction Algorithm Based on Manifold Learning and Its Application to Signal Noise Reduction." Applied Mechanics and Materials 26-28 (June 2010): 653–56. http://dx.doi.org/10.4028/www.scientific.net/amm.26-28.653.

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In the noise reduction algorithm based on manifold learning, phase space data may be distorted and reduction targets are chosen at random, it made efficiency and effect of noise reduction lower.To solve this problem, a improved noise reducation method (local tangent space mean reconstruction) was proposed.The process of global array by affine transformation will be replaced with mean reconstruction,and the intrinsic dimension was estimate as dimension of reduction targets by using maximum likehood estimation method, the data in addition to intrinsic dimension space will be eliminated.Noise reduction experiment to fan vibration signal with noise shows this method had better noise reduction effect.

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48

Jakšić, Olga, Ivana Jokić, Miloš Frantlović, Danijela Randjelović, Dragan Tanasković, Žarko Lazić, and Dana Vasiljević-Radović. "Joint Effect of Heterogeneous Intrinsic Noise Sources on Instability of MEMS Resonators." Electronics ETF 19, no. 2 (July 15, 2016): 59. http://dx.doi.org/10.7251/els1519059j.

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This article's focus is on the numerical estimation of the overall instability of microelectromechanical-system-based (MEMS) resonators, caused by intrinsic noise mechanisms that are different in nature (electrical, mechanical or chemical). Heterogeneous intrinsic noise sources in MEMS resonators that have been addressed here are Johnson–Nyquist noise, 1/f noise, noise caused by temperature fluctuations and adsorptiondesorption induced noise. Their models are given first (based on analytical modeling or based on empirical expressions with experimentally obtained parameters). Then it is shown how each one contributes to the phase noise, a unique figure of merit of resonators instability. Material dependent constants  and knee position in noise spectrum, needed for empirical formulae referring to 1/f noise, have been obtained experimentally, by measurements of noise of MEMS components produced in the Centre of Microelectronic Technologies of the Institute of Chemistry, Technology and Metallurgy in Belgrade. According to these measurements,  varies in the range from 0.776.10-4 to 2.26.10-4 and cut off frequency for 1/f noise varies from 147 Hz to 1 kHz. The determined values are then used for the modeling of micro-resonator phase noise with electrical origin and overall phase noise of a micro-resonator. Numerical example for calculation of overall phase noise is given for a micro-cantilever, produced by the same technology as measured components. The outlined noise analysis can be easily extended and applied to noise analysis of MEMS resonator of an arbitrary shape.

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ADORNO, D. PERSANO, M. C. CAPIZZO, and M. ZARCONE. "CHANGES OF ELECTRONIC NOISE INDUCED BY OSCILLATING FIELDS IN BULK GaAs SEMICONDUCTORS." Fluctuation and Noise Letters 08, no. 01 (March 2008): L11—L22. http://dx.doi.org/10.1142/s0219477508004222.

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A Monte Carlo study of hot-electron intrinsic noise in a n-type GaAs bulk driven by one or two mixed cyclostationary electric fields is presented. The noise properties are investigated by computing the spectral density of velocity fluctuations. An analysis of the noise features as a function of the amplitudes and frequencies of two applied fields is presented. Numerical results show that it is possible to reduce the intrinsic noise. The best conditions to realize this effect are discussed.

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Dolabdjian, Christophe, Basile Dufay, Sebastien Saez, Arthur Yelon, and David Ménard. "Is Low Frequency Excess Noise of GMI Induced by Magnetization Fluctuations?" Key Engineering Materials 605 (April 2014): 437–40. http://dx.doi.org/10.4028/www.scientific.net/kem.605.437.

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We have investigated the possible impact of low frequency magnetization fluctuations on the equivalent magnetic excess noise of GMI sensors, which we have recently shown to exhibit 1/f noise. This noise component is not associated with the detailed measuring setup nor with the conditioning electronic noise sources, suggesting that it is intrinsic to the sensing element. Various intrinsic GMI noise sources might be able to explain this observation; these include magnetic domain wall motion, hysteresis loop losses, etc. Since GMI elements are excited by a high frequency current, it has been assumed that these low frequency (lf) intrinsic noise sources cannot interact with the carrier and the sensed signal. We recall that the GMI effect is based on an impedance variation, which is governed by magnetization angle variations. These are modulated by the sensed signal and the lf magnetization noise, which then appear as sidebands around the carrier frequency. Applying the fluctuation-dissipation theorem to the GMI model, we have related both the signal and noise to the magnetic susceptibility spectrum and thus quantified the equivalent magnetic noise of GMI sensors at lf. We then present a preliminary comparison to our previous experimental results.

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