Relevant bibliographies by topics / Gaussian noise

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Gaussian noise'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 July 2025

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Gaussian noise.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Journal articles on the topic "Gaussian noise"

1

Shi, Yao-Wu, Chen Wang, Lan-Xiang Zhu, Li-Fei Deng, Yi-Ran Shi та 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, № 1 (2018): 18–35. http://dx.doi.org/10.1177/1461348418813291.

Full text
Abstract:
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 distr
APA, Harvard, Vancouver, ISO, and other styles
2

R, Gayathri. "EFFICIENT NON-LOCAL AVERAGING ALGORITHM FOR MEDICAL IMAGES FOR IMPROVED VISUAL QUALITY." ICTACT Journal on Image and Video Processing 11, no. 2 (2020): 2306–9. https://doi.org/10.21917/ijivp.2020.0327.

Full text
Abstract:
Image can be distorted by various ways including sensor inadequacy, transmission error, different noise factors and motion blurring. For controlling and maintaining the visual quality level of the image to be very high, it is very important to improve the image acquisition, image storage and image transmission, etc. Achieving high Peak Signal to Noise Ratio (PSNR) is essential goal of image restoration. This involves removing noises present in the image. Non-Local Means algorithm combined with Laplacian of Gaussian filter finds better results and produces good PSNR against impulse noise as wel
APA, Harvard, Vancouver, ISO, and other styles
3

Qin, Feng Qing, Li Hong Zhu, Li Lan Cao, and Wa Nan Yang. "Gaussian Noised Single-Image Super Resolution Reconstruction." Applied Mechanics and Materials 457-458 (October 2013): 1032–36. http://dx.doi.org/10.4028/www.scientific.net/amm.457-458.1032.

Full text
Abstract:
A framework is proposed to reconstruct a super resolution image from a single low resolution image with Gaussian noise. The degrading processes of Gaussian blur, down-sampling, and Gaussian noise are all considered. For the low resolution image, the Gaussian noise is reduced through Wiener filtering algorithm. For the de-noised low resolution image, iterative back projection algorithm is used to reconstruct a super resolution image. Experiments show that de-noising plays an important part in single-image super resolution reconstruction. In the super reconstructed image, the Gaussian noise is r
APA, Harvard, Vancouver, ISO, and other styles
4

Li, Yongsong, Zhengzhou Li, Kai Wei, Weiqi Xiong, Jiangpeng Yu, and Bo Qi. "Noise Estimation for Image Sensor Based on Local Entropy and Median Absolute Deviation." Sensors 19, no. 2 (2019): 339. http://dx.doi.org/10.3390/s19020339.

Full text
Abstract:
Noise estimation for image sensor is a key technique in many image pre-processing applications such as blind de-noising. The existing noise estimation methods for additive white Gaussian noise (AWGN) and Poisson-Gaussian noise (PGN) may underestimate or overestimate the noise level in the situation of a heavy textured scene image. To cope with this problem, a novel homogenous block-based noise estimation method is proposed to calculate these noises in this paper. Initially, the noisy image is transformed into the map of local gray statistic entropy (LGSE), and the weakly textured image blocks
APA, Harvard, Vancouver, ISO, and other styles
5

YANG, ZHIHUI, and JINQIAO DUAN. "AN INTERMEDIATE REGIME FOR EXIT PHENOMENA DRIVEN BY NON-GAUSSIAN LÉVY NOISES." Stochastics and Dynamics 08, no. 03 (2008): 583–91. http://dx.doi.org/10.1142/s0219493708002469.

Full text
Abstract:
A dynamical system driven by non-Gaussian Lévy noises of small intensity is considered. The first exit time of solution orbits from a bounded neighborhood of an attracting equilibrium state is estimated. For a class of non-Gaussian Lévy noises, it is shown that the mean exit time is asymptotically faster than exponential (the well-known Gaussian Brownian noise case) but slower than polynomial (the stable Lévy noise case), in terms of the reciprocal of the small noise intensity.
APA, Harvard, Vancouver, ISO, and other styles
6

Choudhary, Pandava Himanshu, and T. Satya Savithri. "VLSI Architecture for Decision Tree Based Noise Detector and Gaussian Filter for Noise Removal." Journal of VLSI Design and Signal Processing 11, no. 2 (2025): 1–19. https://doi.org/10.46610/jovdsp.2025.v11i02.001.

Full text
Abstract:
This paper focuses on noise removal using a hybrid approach that integrates a decision-tree-based noise detector with a Gaussian filter to reduce Gaussian noise while preserving important image details. Gaussian noise, characterized by random intensity variations, often leads to a loss in visual quality. Conventional filtering applies uniform smoothing, which can cause blurring of edges and textures. To overcome this, a decision tree composed of three modules uniformity analyzer, edge transition detector, and neighborhood correlation evaluator is used to accurately identify noisy pixels. Only
APA, Harvard, Vancouver, ISO, and other styles
7

Shi, Gui Cun, and Fei Xing Wang. "Mixed Noise Image De-Noising Based on EM Algorithm." Applied Mechanics and Materials 556-562 (May 2014): 4734–41. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.4734.

Full text
Abstract:
Obtaining high quality images is very important in many areas of applied sciences, but images are usually polluted by noise in the process of generation, transmission and acquisition. In recent years, wavelet analysis achieves significant results in the field of image de-noising. However, most of the studies of noise-induced phenomena assume that the noise source is Gaussian. The use of mixed Gaussian and impulse noise is rare, mainly because of the difficulties in handling them. In the process of image de-noising, the noise model’s parameter estimation is a key issue, because the accuracy of
APA, Harvard, Vancouver, ISO, and other styles
8

Jain, Anshika, and Maya Ingle. "PERFORMANCE ANALYSIS OF NOISE REMOVAL TECHNIQUES FOR FACIAL IMAGES- A COMPARATIVE STUDY." BSSS journal of computer 12, no. 1 (2021): 1–10. http://dx.doi.org/10.51767/jc1201.

Full text
Abstract:
Image de-noising has been a challenging issue in the field of digital image processing. It involves the manipulation of image data to produce a visually high quality image. While maintaining the desired information in the quality of an image, elimination of noise is an essential task. Various domain applications such as medical science, forensic science, text extraction, optical character recognition, face recognition, face detection etc. deal with noise removal techniques. There exist a variety of noises that may corrupt the images in different ways. Here, we explore filtering techniques viz.
APA, Harvard, Vancouver, ISO, and other styles
9

Kang, Hyekyoung, Chanrok Park, and Hyungjin Yang. "Evaluation of Denoising Performance of ResNet Deep Learning Model for Ultrasound Images Corresponding to Two Frequency Parameters." Bioengineering 11, no. 7 (2024): 723. http://dx.doi.org/10.3390/bioengineering11070723.

Full text
Abstract:
Ultrasound imaging is widely used for accurate diagnosis due to its noninvasive nature and the absence of radiation exposure, which is achieved by controlling the scan frequency. In addition, Gaussian and speckle noises degrade image quality. To address this issue, filtering techniques are typically used in the spatial domain. Recently, deep learning models have been increasingly applied in the field of medical imaging. In this study, we evaluated the effectiveness of a convolutional neural network-based residual network (ResNet) deep learning model for noise reduction when Gaussian and speckl
APA, Harvard, Vancouver, ISO, and other styles
10

Zhou, Yuqian, Jianbo Jiao, Haibin Huang, Jue Wang, and Thomas Huang. "Adaptation Strategies for Applying AWGN-Based Denoiser to Realistic Noise." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 10085–86. http://dx.doi.org/10.1609/aaai.v33i01.330110085.

Full text
Abstract:
Discriminative learning based denoising model trained with Additive White Gaussian Noise (AWGN) performs well on synthesized noise. However, realistic noise can be spatialvariant, signal-dependent and a mixture of complicated noises. In this paper, we explore multiple strategies for applying an AWGN-based denoiser to realistic noise. Specifically, we trained a deep network integrating noise estimating and denoiser with mixed Gaussian (AWGN) and Random Value Impulse Noise (RVIN). To adapt the model to realistic noises, we investigated multi-channel, multi-scale and super-resolution approaches.
APA, Harvard, Vancouver, ISO, and other styles
More sources

Dissertations / Theses on the topic "Gaussian noise"

1

Schlag, Adam Wayne. "DEVELOPMENT AND MODIFICATION OF A GAUSSIAN AND NON-GAUSSIAN NOISE EXPOSURE SYSTEM." OpenSIUC, 2012. https://opensiuc.lib.siu.edu/theses/973.

Full text
Abstract:
Millions of people across the world currently have noise induced hearing loss, and many are working in conditions with both continuous Gaussian and non-Gaussian noises that could affect their hearing. It was hypothesized that the energy of the noise was the cause of the hearing loss and did not depend on temporal pattern of a noise. This was referred to as the equal energy hypothesis. This hypothesis has been shown to have limitations though. This means that there is a difference in the types of noise a person receives to induce hearing loss and it is necessary to build a system that can easil
APA, Harvard, Vancouver, ISO, and other styles
2

Sung, Youngkyu. "Non-Gaussian noise spectroscopy with superconducting qubits." Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/120365.

Full text
Abstract:
Thesis: S.M. in Computer Science and Engineering, Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018.<br>This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (pages 91-95).<br>Most quantum control and quantum error-correction protocols assume that the noise causing decoherence is described by Gaussian statistics. However, the Gaussianity assumption break
APA, Harvard, Vancouver, ISO, and other styles
3

Gaerke, Tiffani M. "Characteristic Functions and Bernoulli-Gaussian Impulsive Noise Channels." University of Akron / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=akron1408040080.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Watson, Stephen M. "Frequency demodulation in the presence of multiplicative speckle noise." Thesis, University of Nottingham, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.246382.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Zoghi, Masrour. "Regret bounds for Gaussian process bandits without observation noise." Thesis, University of British Columbia, 2012. http://hdl.handle.net/2429/42865.

Full text
Abstract:
This thesis presents some statistical refinements of the bandits approach presented in [11] in the situation where there is no observation noise. We give an improved bound on the cumulative regret of the samples chosen by an algorithm that is related (though not identical) to the UCB algorithm of [11] in a complementary setting. Given a function f on a domain D ⊆ R^d , sampled from a Gaussian process with an anisotropic kernel that is four times differentiable at 0, and a lattice L ⊆ D, we show that if the points in L are chosen for sampling using our branch-and-bound algorithm, the regret asy
APA, Harvard, Vancouver, ISO, and other styles
6

Aouini, Sadok. "A programmable analog Gaussian noise generator for test applications /." Thesis, McGill University, 2006. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=99401.

Full text
Abstract:
This thesis presents a robust programmable analog Gaussian noise generator suitable for mixed-signal test applications. Unlike conventional methods, noise generators employing a linear feedback shift register (LFSR) or resistor thermal noise amplification techniques, the user has full control over the characteristics of the Gaussian signal. Indeed, the frequency band, the mean, and the variance of the distribution are fully programmable over the voltage range within the supply rails. The method consists of digitally encoding the specified Gaussian signal in a random access memory (RAM), using
APA, Harvard, Vancouver, ISO, and other styles
7

Jezierska, Anna Maria. "Image restoration in the presence of Poisson-Gaussian noise." Phd thesis, Université Paris-Est, 2013. http://tel.archives-ouvertes.fr/tel-00906718.

Full text
Abstract:
This thesis deals with the restoration of images corrupted by blur and noise, with emphasis on confocal microscopy and macroscopy applications. Due to low photon count and high detector noise, the Poisson-Gaussian model is well suited to this context. However, up to now it had not been widely utilized because of theoretical and practical difficulties. In view of this, we formulate the image restoration problem in the presence of Poisson-Gaussian noise in a variational framework, where we express and study the exact data fidelity term. The solution to the problem can also be interpreted as a Ma
APA, Harvard, Vancouver, ISO, and other styles
8

Hocquet, Antoine. "The Landau-Lifshitz-Gilbert equation driven by Gaussian noise." Palaiseau, Ecole polytechnique, 2015. https://theses.hal.science/tel-01265433/document.

Full text
Abstract:
Cette thèse porte sur l'influence d'un bruit Gaussien dans l'équation de Landau-Lifshitz-Gilbert Stochastique (SLLG). Il s'agit d'une équation aux dérivées partielles stochastique, non linéaire, avec une contrainte non convexe sur le module des solutions. Le chapitre 1 se consacre tout d'abord à la solvabilité locale de SLLG. Utilisant les propriétés classiques de l'intégration stochastique dans un espace de Banach, nous proposons une formulation mild, et donnons l'existence et l'unicité d'une solution locale en dimension quelconque, pour un bruit Gaussien régulier en espace, dans le cas sur-a
APA, Harvard, Vancouver, ISO, and other styles
9

Napier, T. M., and R. A. Peloso. "A PREDICTABLE PERFORMANCE WIDEBAND NOISE GENERATOR." International Foundation for Telemetering, 1990. http://hdl.handle.net/10150/613762.

Full text
Abstract:
International Telemetering Conference Proceedings / October 29-November 02, 1990 / Riviera Hotel and Convention Center, Las Vegas, Nevada<br>An innovative digital approach to analog noise synthesis is described. This method can be used to test bit synchronizers and other communications equipment over a wide range of data rates. A generator has been built which has a constant RMS output voltage and a well-defined, closely Gaussian amplitude distribution. Its frequency spectrum is flat within 0.3 dB from dc to an upper limit which can be varied from 1 Hz to over 100 MHz. Both simulation and prac
APA, Harvard, Vancouver, ISO, and other styles
10

Green, Donald R. "The utility of higher-order statistics in Gaussian noise suppression." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2003. http://library.nps.navy.mil/uhtbin/hyperion-image/03Mar%5FGreen.pdf.

Full text
Abstract:
Thesis (M.S. in Electrical Engineering)--Naval Postgraduate School, March 2003.<br>Thesis advisor(s): Charles W. Therrien, Charles W. Granderson. Includes bibliographical references (p. 123). Also available online.
APA, Harvard, Vancouver, ISO, and other styles
More sources

Books on the topic "Gaussian noise"

1

Kassam, Saleem A. Signal Detection in Non-Gaussian Noise. Springer New York, 1988. http://dx.doi.org/10.1007/978-1-4612-3834-8.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Kassam, Saleem A. Signal Detection in Non-Gaussian Noise. Springer New York, 1988.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
3

1925-, Thomas John Bowman, ed. Signal detection in non-Gaussian noise. Springer-Verlag, 1988.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
4

Obata, Nobuaki. White noise calculus and Fock space. Springer-Verlag, 1994.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
5

Hida, Takeyuki. Lectures on white noise functionals. World Scientific, 2008.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
6

Gualtierotti, Antonio F. Detection of Random Signals in Dependent Gaussian Noise. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-22315-5.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Hippenstiel, Ralph Dieter. Analysis using Bi-spectral related technique. Naval Postgraduate School, 1993.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
8

Middleton, D. First-order non-gaussian class C interference models and their associated threshold detection algorithms. U.S. Dept. of Commerce, National Telecommunications and Information Administration, 1987.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
9

Middleton, D. First-order non-gaussian class C interference models and their associated threshold detection algorithms. U.S. Dept. of Commerce, National Telecommunications and Information Administration, 1987.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
10

Middleton, D. First-order non-gaussian class C interference models and their associated threshold detection algorithms. U.S. Dept. of Commerce, National Telecommunications and Information Administration, 1987.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
More sources

Book chapters on the topic "Gaussian noise"

1

Hida, Takeyuki, Hui-Hsiung Kuo, Jürgen Potthoff, and Ludwig Streit. "Gaussian Spaces." In White Noise. Springer Netherlands, 1993. http://dx.doi.org/10.1007/978-94-017-3680-0_1.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Seshadri, V. "Traffic noise intensity." In The Inverse Gaussian Distribution. Springer New York, 1999. http://dx.doi.org/10.1007/978-1-4612-1456-4_20.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Gualtierotti, Antonio F. "Likelihoods for Signal Plus Gaussian Noise Versus Gaussian Noise." In Detection of Random Signals in Dependent Gaussian Noise. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-22315-5_17.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Weik, Martin H. "additive white Gaussian noise." In Computer Science and Communications Dictionary. Springer US, 2000. http://dx.doi.org/10.1007/1-4020-0613-6_319.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Walrand, Jean. "Route Planning: B." In Probability in Electrical Engineering and Computer Science. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-49995-2_14.

Full text
Abstract:
AbstractThere is a class of control problems that admit a particularly elegant solution: the linear quadratic Gaussian (LQG) problems. In these problems, the state dynamics and observations are linear, the cost is quadratic, and the noise is Gaussian. Section 14.1 explains the theory of LQG problems when one observes the state. Section 14.2 discusses the situation when the observations are noisy and shows the remarkable certainty equivalence property of the solution. Section 14.3 explains how noisy observations affect Markov decision problems.
APA, Harvard, Vancouver, ISO, and other styles
6

Gualtierotti, Antonio F. "Likelihoods for Signal Plus “White Noise” Versus “White Noise”." In Detection of Random Signals in Dependent Gaussian Noise. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-22315-5_13.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Machell, Fredrick W., Clark S. Penrod, and Glen E. Ellis. "Statistical Characteristics of Ocean Acoustic Noise Processes." In Topics in Non-Gaussian Signal Processing. Springer New York, 1989. http://dx.doi.org/10.1007/978-1-4613-8859-3_3.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Kostková, Jitka, and Jan Flusser. "Robust Histogram Estimation Under Gaussian Noise." In Computer Analysis of Images and Patterns. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-29888-3_34.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Giulini, Saverio. "Some Remarks on Generalized Gaussian Noise." In Trends in Harmonic Analysis. Springer Milan, 2013. http://dx.doi.org/10.1007/978-88-470-2853-1_11.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Levy, Bernard C. "Wiener Process and White Gaussian Noise." In Random Processes with Applications to Circuits and Communications. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-22297-0_6.

Full text
APA, Harvard, Vancouver, ISO, and other styles

Conference papers on the topic "Gaussian noise"

1

Spaulding, A. D. "Non-Gaussian Noise in Communication." In 10th International Zurich Symposium and Technical Exhibition on Electromagnetic Compatibility. IEEE, 1993. https://doi.org/10.23919/emc.1993.10781171.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Diop, El Hadji S., Abdel-O. Boudraa, and Ndéye N. Gueye. "2D Teager-Kaiser Analysis on Gaussian Noise." In 2024 32nd European Signal Processing Conference (EUSIPCO). IEEE, 2024. http://dx.doi.org/10.23919/eusipco63174.2024.10715070.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Yin, Chao. "Research on RAIM algorithm based on Gaussian mixture model under non-Gaussian noise." In Third International Conference on Geographic Information and Remote Sensing Technology (GIRST 2024), edited by Francesco Benedetto, Fabio Tosti, and Roman Alvarez. SPIE, 2025. https://doi.org/10.1117/12.3059892.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Shapiro, Jeffrey H. "Quantum Gaussian noise." In SPIE's First International Symposium on Fluctuations and Noise, edited by Derek Abbott, Jeffrey H. Shapiro, and Yoshihisa Yamamoto. SPIE, 2003. http://dx.doi.org/10.1117/12.504770.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

M, Jeba Jenitha, Kani Jesintha D, and Mahalakshmi P. "Noise Adaptive Fuzzy Switching Median Filters for Removing Gaussian Noise." In The International Conference on scientific innovations in Science, Technology, and Management. International Journal of Advanced Trends in Engineering and Management, 2023. http://dx.doi.org/10.59544/ozsc7243/ngcesi23p113.

Full text
Abstract:
Recently, in all image processing systems, image restoration plays a major role and it forms the major part of image processing systems. Medical images such as brain Magnetic Resonance Imaging (MRI), ultrasound images of liver and kidney, retinal images and images of uterus images are often affected by various types of noises such as Gaussian noise and salt and pepper noise. All image restoration techniques attempts to remove various types of noises. This paper deals with various filters namely Mean Filter, Averaging Filter, Median Filter, Adaptive Median Filter, Adaptive Weighted Median Filte
APA, Harvard, Vancouver, ISO, and other styles
6

Tollkuhn, Andreas, Florian Particke, and Jorn Thielecke. "Gaussian State Estimation with Non-Gaussian Measurement Noise." In 2018 Sensor Data Fusion: Trends, Solutions, Applications (SDF). IEEE, 2018. http://dx.doi.org/10.1109/sdf.2018.8547146.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Kopparapu, Sunil Kumar, and M. Satish. "Identifying Optimal Gaussian Filter for Gaussian Noise Removal." In 2011 Third National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG). IEEE, 2011. http://dx.doi.org/10.1109/ncvpripg.2011.34.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Luegmair, Marinus, Rafaella Dantas, Felix Schneider, and Gerhard Müller. "Gaussian Process Surrogate Models for Vibroacoustic Simulations." In 13th International Styrian Noise, Vibration & Harshness Congress: The European Automotive Noise Conference. SAE International, 2024. http://dx.doi.org/10.4271/2024-01-2930.

Full text
Abstract:
&lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;In vehicle Noise Vibration Harshness (NVH) development, vibroacoustic simulations with Finite Element (FE) Models are a common technique. The computational costs for these calculations are steadily rising due to more detailed modelling and higher frequency ranges. At the same time the need for multiple evaluations of the same model with different input parameters – e.g., for uncertainty quantification, optimization, or robustness investigation – is also increasing.&lt;/div&gt;&lt;div class="htmlview paragraph"&gt;Therefo
APA, Harvard, Vancouver, ISO, and other styles
9

Besson, Olivier, Eric Chaumette, and Francois Vincent. "Adaptive detection of a Gaussian signal in Gaussian noise." In 2015 IEEE 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP). IEEE, 2015. http://dx.doi.org/10.1109/camsap.2015.7383750.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Panigrahi, Susant Kumar, Supratim Gupta, and Prasanna K. Sahu. "Phases under Gaussian additive noise." In 2016 International Conference on Communication and Signal Processing (ICCSP). IEEE, 2016. http://dx.doi.org/10.1109/iccsp.2016.7754471.

Full text
APA, Harvard, Vancouver, ISO, and other styles

Reports on the topic "Gaussian noise"

1

Chow, Winston C. Estimation Theory with Fractional Gaussian Noise. Defense Technical Information Center, 1995. http://dx.doi.org/10.21236/ada301443.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Chi, Eric C., and Tamara Gibson Kolda. Making tensor factorizations robust to non-gaussian noise. Office of Scientific and Technical Information (OSTI), 2011. http://dx.doi.org/10.2172/1011706.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Lin, Feng-Ling, and Ben H. Cantrell. Bounds on Doppler Frequency Estimation in Correlated Nonstationary Gaussian Noise. Defense Technical Information Center, 1988. http://dx.doi.org/10.21236/ada201052.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Sher, David. Optimal Likelihood Generators for Edge Detection under Gaussian Additive Noise. Defense Technical Information Center, 1986. http://dx.doi.org/10.21236/ada179945.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Nuttall, Albert H. Detection Performance of a Suboptimum Processor in Non-Gaussian Noise. Defense Technical Information Center, 2004. http://dx.doi.org/10.21236/ada423872.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Kay, Steven. The Theory of Detection in Incompletely Characterized Non-Gaussian Noise. Defense Technical Information Center, 1985. http://dx.doi.org/10.21236/ada162607.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Kay, Steven, and Debasis Sengupta. Detection in Incompletely Characterized Colored Non-Gaussian Noise via Parametric Modeling. Defense Technical Information Center, 1986. http://dx.doi.org/10.21236/ada175402.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Wilson, Gary R. Detection and Time Delay Estimation of Non-Gaussian Signals in Noise. Defense Technical Information Center, 1990. http://dx.doi.org/10.21236/ada227046.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Cantrell, Ben. Detection of Signals in Non-Gaussian Correlated Noise Derived from Cauchy Processes. Defense Technical Information Center, 1987. http://dx.doi.org/10.21236/ada187488.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Ahn, Hyungsok, and Raisa E. Feldman. Optimal Filtering of a Gaussian Signal in the Presence of Levy Noise. Defense Technical Information Center, 1995. http://dx.doi.org/10.21236/ada336874.

Full text
APA, Harvard, Vancouver, ISO, and other styles
You might also be interested in the extended bibliographies on the topic 'Gaussian noise' for particular source types:
Journal articles Dissertations / Theses Books
We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!

To the bibliography