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Relevant bibliographies by topics / Noise characterization

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Academic literature on the topic 'Noise characterization'

Author: Grafiati

Published: 4 June 2021

Last updated: 17 June 2025

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Journal articles on the topic "Noise characterization"

1

Ji, Un Cig, and Nobuaki Obata. "A Unified Characterization Theorem in White Noise Theory." Infinite Dimensional Analysis, Quantum Probability and Related Topics 06, no. 02 (2003): 167–78. http://dx.doi.org/10.1142/s0219025703001122.

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We revisit a CKS-space from the viewpoint of standard setup of white noise calculus and prove a general characterization theorem for white noise operators from a CKS-space into another CKS-space. The usual characterizations so far obtained for white noise distributions, white noise test functions and white noise operators are all consequences of the unified characterization theorem.

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CHEN, CHIH-HUNG. "ACCURACY ISSUES OF ON-WAFER MICROWAVE NOISE MEASUREMENTS." Fluctuation and Noise Letters 08, no. 03n04 (2008): L281—L303. http://dx.doi.org/10.1142/s0219477508005136.

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The accuracy issues of on-wafer noise characterization for a linear noisy two-port are presented in this paper. It starts with the description of a microwave noise measurement system and the possible source of error due to the microwave power meter in the measurement system. With the description of noise characterization techniques, this paper reviews a couple of methods for noise parameter extraction to handle the errors in the measured noise powers, noise factors, and source admittances. It also presents the methods to extract the physical noise parameters and to take care of different source admittances in the hot and cold states for accuracy enhancement.

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Dobrowolski, Janusz A. "Noise Characterization of Differential Multi- Element Multiport Networks - the Wave Approach." International Journal of Electronics and Telecommunications 61, no. 4 (2015): 395–401. http://dx.doi.org/10.2478/eletel-2015-0052.

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Abstract In this paper there is presented and discussed a general analysis method for noise characterization of noisy multielement multiport differential networks. It is based on mixed mode, differential and common mode, noise waves representation of noise, generalized mixed-mode scattering parameters and generalized mixed-mode noise wave correlation parameters for the network. There are derived analytical relation between the noise figure for a given output port and the noise matrix and the scattering parameters of the network, as well as the correlations between the input port noise waves. The signal to noise ratio degradation factor is derived and discussed, too. Presented results can be implemented directly in a CAD software for noise analysis of differential microwave multi-element multiport networks with differential as well as with conventional single ended ports.

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Masoumi, Moien, Abeka Selliah, and Berker Bilgin. "Development of an Experimental Acoustic Noise Characterization Setup for Electric Motor Drive Applications." Energies 17, no. 21 (2024): 5371. http://dx.doi.org/10.3390/en17215371.

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This paper presents the development of an experimental setup for acoustic noise characterization of electric motors. It describes the sound measurement microphones utilized in the setup and discusses the application of octave bands and A-weighting in noise measurement. Various methods for acoustic noise measurement and sound power calculation, including those based on sound pressure and sound intensity, are also covered. Given the relatively noisy test environment and restricted access around the test setup, discrete point sound intensity measurement is selected for sound power calculation. Initially, a stationary probe-holding fixture is designed and fabricated for sound intensity measurements. To enhance the fixture’s flexibility and the accuracy of the measurements, a transportable fixture is subsequently designed and fabricated. The necessary hardware and software settings for acoustic noise characterization are then developed. Finally, the setup is used to conduct acoustic noise characterization of an IPM motor, validating the application of the transportable probe-holding fixture.

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Paprocki, Carter A., Sriram Malladi, and Andrew Barnard. "Hydraulic flow noise characterization of spool valves using experimental techniques." Journal of the Acoustical Society of America 153, no. 3_supplement (2023): A111. http://dx.doi.org/10.1121/10.0018338.

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This research was conducted to characterize and develop a deeper understanding of the hydraulic flow noise for a spool valve. Hydraulic noise was analyzed to study the interaction of the valve spool and the flow noise generation to isolate where in the spool articulation the most noise was generated. The goal was to correlate turbulent fluid flow in pressurized hydraulic circuits to different spool positions while isolating what factors were the main drivers of spool noise generation. The experiments were conducted on a valve controlled by a closed-circuit hydraulic system, using this system fluid-born noise could be isolated from other noisy mechanisms and a noise profile of each could be measured. It was found that internal spool geometry and flow channel open area played the largest role in valve noise generation for the valve. In the future, more valves could be tested to develop a robust profile library using the test methods developed.

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CHUNG, DONG MYUNG, UN CIG JI, and NOBUAKI OBATA. "QUANTUM STOCHASTIC ANALYSIS VIA WHITE NOISE OPERATORS IN WEIGHTED FOCK SPACE." Reviews in Mathematical Physics 14, no. 03 (2002): 241–72. http://dx.doi.org/10.1142/s0129055x0200117x.

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White noise theory allows to formulate quantum white noises explicitly as elemental quantum stochastic processes. A traditional quantum stochastic differential equation of Itô type is brought into a normal-ordered white noise differential equation driven by lower powers of quantum white noises. The class of normal-ordered white noise differential equations covers quantum stochastic differential equations with highly singular noises such as higher powers or higher order derivatives of quantum white noises, which are far beyond the traditional Itô theory. For a general normal-ordered white noise differential equation unique existence of a solution is proved in the sense of white noise distribution. Its regularity properties are investigated by means of weighted Fock spaces interpolating spaces of white noise distributions and associated characterization theorems for S-transform and for operator symbols.

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Langampol, Kriengkri, Kanabadee Srisomboon, Vorapoj Patanavijit, and Wilaiporn Lee. "Smart Switching Bilateral Filter with Estimated Noise Characterization for Mixed Noise Removal." Mathematical Problems in Engineering 2019 (May 28, 2019): 1–23. http://dx.doi.org/10.1155/2019/5632145.

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Traditionally, several existing filters are proposed for removing a specific type of noise. However, in practice, the image communicated through the communication channel may be contaminated with more than one type of noise. Switching bilateral filter (SBF) is proposed for removing mixed noise by detecting a contaminated noise at the concerned pixel and recalculates the filter parameters. Although the filter parameters of SBF are sensitive to type and strength of noise, the traditional SBF filter has not taken the strength into account. Therefore, the traditional SBF filter cannot remove the mixed noise efficiently. In this paper, we propose a smart switching bilateral filter (SSBF) to outperform a demerit of traditional SBF filter. In the first stage of SSBF, we propose a new scheme of noise estimation using domain weight (DW) pattern which characterizes the distribution of the different intensity between a considered pixel and its neighbors. By using this estimation, the types of mixed noises and their strength are estimated accurately. The filter parameters of SBF are selected from the table where the spatial weight and radiometric weight are already learned. As a result, SSBF can improve the performance of traditional SBF and can remove mixed noises efficiently without knowing the exact type of contaminated mixed noise. Moreover, the performance of SSBF is compared to the optimal SBF filter (OSBF) where OSBF sets the optimal value of filter parameters on the contaminated mixed noise and three new filters — block-matching and 3D filtering (BM3D), nonlocal sparse representation (NCSR), and trilateral filter (TF). The simulation results showed that the performance of SSBF outperforms BM3D, NCSR, TF, and SBF and is near to optimal SBF filter, even if the SSBF does not know the type of mixed noise.

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Josephine, S., and S. Murugan. "Noise Removal from Brain MRI Images Using Adaptive Bayesian Shrinkage." Journal of Computational and Theoretical Nanoscience 17, no. 4 (2020): 1818–25. http://dx.doi.org/10.1166/jctn.2020.8446.

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In MR machine, surface coils, especially phased-arrays are used extensively for acquiring MR images with high spatial resolution. The signal intensities on images acquired using these coils have a non-uniform map due to coil sensitivity profile. Although these smooth intensity variations have little impact on visual diagnosis, they become critical issues when quantitative information is needed from the images. Sometimes, medical images are captured by low signal to noise ratio (SNR). The low SNR makes it difficult to detect anatomical structures because tissue characterization fails on those images. Hence, denoising are essential processes before further processing or analysis will be conducted. They found that the noise in MR image is of Rician distribution. Hence, general filters cannot be used to remove these types of noises. The linear spatial filtering technique blurs the object boundaries and degrades the sharp details. The existing works proved that Wavelet based works eliminates the noise coefficient that called wavelet thresholding. Wavelet thresholding estimates the noise level from high frequency content and estimates the threshold value by comparing the estimated noisy wavelet coefficient with other wavelet coefficients and eliminate the noisy pixel intensity value. Bayesian Shrinkage rule is one of the widely used methods. It uses for Gaussian type of noise, the proposed method introduced some adaptive technique in Bayesian Shrinkage method to remove Rician type of noises from MRI images. The results were verified using quantitative parameters such as Peak Signal to Noise Ratio (PSNR). The proposed Adaptive Bayesian Shrinkage Method (ABSM) outperformed existing methods.

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CADDEMI, ALINA, and NICOLA DONATO. "ON THE NOISE RESISTANCE OF FIELD-EFFECT TRANSISTORS AT MICROWAVE FREQUENCIES." Fluctuation and Noise Letters 01, no. 03 (2001): R151—R161. http://dx.doi.org/10.1142/s0219477501000342.

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This paper presents a survey on the topical aspects of the noise resistance in field-effect transistors (FET) at microwave frequencies. Such noise parameter represents the sensitivity of the device noise figure to the departure from the minimum noise condition and is therefore important in all low-noise applications. The performance of the noise resistance in FETs has been reviewed since the first noise modeling analysis of short-gate devices were presented in the early '70s. The authors also comment and compare their own results on this subject as obtained by extensive experimental activity in the field of noisy device characterization vs. frequency, bias and temperature conditions.

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Shin, Jaehyun, Yongmin Zhong, and Chengfan Gu. "Real-Time Nonlinear Characterization of Soft Tissue Mechanical Properties." Journal of Sensors 2020 (March 4, 2020): 1–15. http://dx.doi.org/10.1155/2020/9873410.

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Online soft tissue characterization is important for robotic-assisted minimally invasive surgery to achieve precise and stable robotic control with haptic feedback. This paper presents a new nonlinear recursive adaptive filtering methodology for online nonlinear soft tissue characterization. An adaptive unscented Kalman filter is developed based on the Hunt-Crossley model by windowing approximation to online estimate system and measurement noise covariances. To improve the accuracy of noise covariance estimations, a recursive formulation is subsequently developed for estimation of system and measurement noise covariances by introducing a weighting factor. This weighting factor is further modified to accommodate noise statistics of large variation which could be caused by rupture events and geometric discontinuities in robotic-assisted surgery. Simulations, experiments, and comparison analyses demonstrate that the proposed nonlinear recursive adaptive filtering methodology can characterize soft tissue parameters in the presence of system or measurement noise statistics in both small and large variations for robotic-assisted surgery. The proposed methodology can effectively estimate soft tissue parameters under system and measurement noises in both small and large variations, leading to improved filtering accuracy and robustness in comparison with UKF.

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Dissertations / Theses on the topic "Noise characterization"

1

Béland, Paul. "Millimeter-wave noise characterization of PHEMT devices." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/mq26300.pdf.

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Moeller, Michael M. Jr. "Noise environment characterization in military treatment facilities." Thesis, Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/48995.

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Hospital sound environments are complex and hard to understand. One of the most important factors in these environments is the effective communication between staff members in regards to patient care and successful communication depends in part on the hospital’s sound environment. In this study, objective sound measurements as well as occupant perceptive data were collected at three hospitals. Sound pressure levels; including maximum, peak, minimum and equivalent levels were recorded in these hospitals, in addition to active impulse response measurements. Acoustic descriptors of the sound environment such as spectral content, level distributions, energy decay and temporal patterns were examined. The perception of the hospital soundscape (sound environment) was evaluated through surveys of the staff, patients and visitors to units. It was found that noise levels in all patient rooms and work areas were significantly higher than guidelines laid out in previous literature and by professional organizations. This work contributes to the field by broadening the metrics used to quantify hospital acoustic environments. In addition, this work added to the field by providing the most rigorous acoustic field measurement set published to date. This was done to create an accurate portrayal of the hospital soundscape environment.

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Dieme, Robert. "Characterization of noise in MEMS piezoresistive microphones." [Gainesville, Fla.] : University of Florida, 2005. http://purl.fcla.edu/fcla/etd/UFE0010508.

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Rai, Arunkumar R. "Characterization of noise and design of active noise control technology in longwall mines." Morgantown, W. Va. : [West Virginia University Libraries], 2005. https://eidr.wvu.edu/etd/documentdata.eTD?documentid=4412.

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Thesis (M.S.)--West Virginia University, 2005.<br>Title from document title page. Document formatted into pages; contains xi, 65 p. : ill. (some col.). Vita. Includes abstract. Includes bibliographical references (p. 63-64).

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Brown, Colin. "A Comprehensive Noise Characterization in a High School." University of Toledo Health Science Campus / OhioLINK, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=mco1290020463.

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Ingemanson, Megan Lynn. "Experimental Characterization of Wind Turbine Blade Aerodynamic Noise." Thesis, University of California, Davis, 2013. http://pqdtopen.proquest.com/#viewpdf?dispub=1539643.

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<p> Wind turbine noise at low frequencies less than 300Hz is not only annoying to humans but has been proven to cause serious health issues. Additionally, animals are severely affected by wind turbines because a small increase in ambient noise (as is produced by wind turbines) significantly reduces their listening ability. In an attempt to better understand and characterize the aerodynamic noise of wind turbine blades, experimental testing was completed on PowerWorks 100kW and GudCraft WG700 blade specimens in the University of California, Davis Transportation Noise Control Center's anechoic chamber. Experimental testing and data analysis proved approximately 4.0dB to 6.0dB was produced due to the blades' geometric design for both blade specimens at low frequencies. This noise was maximized at the blades' leading edge along the central portion of the blades' radius. Theoretical prediction models have been used to determine that, for typical wind speeds and low frequencies, noise generated due to the tip passing frequency is clearly predominant.</p>

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Lentz, William Alexander 1976. "Characterization of noise in uncooled IR bolometer arrays." Thesis, Massachusetts Institute of Technology, 1998. http://hdl.handle.net/1721.1/28199.

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Thesis (M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1998.<br>Includes bibliographical references (p. 81-82).<br>by William Alexander Lentz.<br>M.Eng.

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Romero, Dominguez Saul. "Noise and electrical characterization in magnetic tunnel junctions." Thesis, University of Cambridge, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.611223.

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Dubin, Stacie R. Dubin. "Characterization and Perceptions of Noise in Nursing Homes." University of Toledo / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1513362995616053.

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Krueger, David William. "Array-Based Characterization of Military Jet Aircraft Noise." BYU ScholarsArchive, 2012. https://scholarsarchive.byu.edu/etd/3676.

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Since the 1950s the jet aeroacoustics community has been involved in predicting and measuring the noise distribution in jets. In this work, cylindrical and planar Fourier near-field acoustical holography are used to investigate radiation from a full-scale, installed jet engine. Practical problems involving measurement aperture and the highly directional nature of the source are addressed. Insights from numerical simulations reveal usable reconstruction regions. A comparison of cylindrical and planar NAH for the respective measurement apertures shows cylindrical NAH outperforms planar NAH on reconstructions both towards and away from the source.

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Books on the topic "Noise characterization"

1

National Renewable Energy Laboratory (U.S.), ed. Acoustic array development for wind turbine noise characterization. National Renewable Energy Laboratory, 2013.

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T, Logan Ronald, and United States. National Aeronautics and Space Administration., eds. Semiconductor laser low frequency noise characterization: Final technical report. Rome Laboratory, Air Force Materiel Command, 1996.

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L, Alford William, and United States. National Aeronautics and Space Administration. Scientific and Technical Information Division., eds. Landsat-4 and Landsat-5 multispectral scanner coherent noise characterization and removal. National Aeronautics and Space Administration, Scientific and Technical Information Division, 1989.

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L, Alford William, and United States. National Aeronautics and Space Administration. Scientific and Technical Information Branch., eds. Landsat-4 and Landsat-5 multispectral scanner coherent noise characterization and removal. National Aeronautics and Space Administration, Scientific and Technical Information Branch, 1986.

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L, Alford William, and United States. National Aeronautics and Space Administration. Scientific and Technical Information Branch., eds. Landsat-4 and Landsat-5 multispectral scanner coherent noise characterization and removal. National Aeronautics and Space Administration, Scientific and Technical Information Branch, 1986.

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6

Huber, Alex. Noise characterization and modeling of InP/InGaAs HBTs for RF circuit design. Hartung-Gorre, 2000.

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Tilton, James C. Landsat-4 and Landsat-5 multispectral scanner coherent noise characterization and removal. Goddard Space Flight Center, 1988.

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L, Alford William, and United States. National Aeronautics and Space Administration. Scientific and Technical Information Division., eds. Landsat-4 and Landsat-5 multispectral scanner coherent noise characterization and removal. National Aeronautics and Space Administration, Scientific and Technical Information Division, 1989.

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9

Ong, Peijie. Optimization and characterization of noise for ion channel and carbon nanotube biosensing platforms. [publisher not identified], 2020.

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International Symposium on Environmental Annoyance (1986 Woudschoten, Netherlands). Environmental annoyance: Characterization, measurement, and control : proceedings of the International Symposium on Environmental Annoyance, held at the Conference Centre Woudschoten, the Netherlands, 15-18 September 1986. Edited by Koelega Harry S. Elsevier Science Publishers, 1987.

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Book chapters on the topic "Noise characterization"

1

Baumgartner, Jeremy, Markus Hinsche, and Roberto Manduchi. "Empirical Characterization of Camera Noise." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38989-4_1.

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Ishihara, M., T. Sakamoto, and H. Minami. "Grain Size Measurement Using Barkhausen Noise Method." In Nondestructive Characterization of Materials IV. Springer US, 1991. http://dx.doi.org/10.1007/978-1-4899-0670-0_33.

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Tsuchiya, Toshiaki. "Detection and Characterization of Single Defects in MOSFETs." In Noise in Nanoscale Semiconductor Devices. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-37500-3_11.

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Stampfer, Bernhard, Alexander Grill, and Michael Waltl. "Advanced Electrical Characterization of Single Oxide Defects Utilizing Noise Signals." In Noise in Nanoscale Semiconductor Devices. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-37500-3_7.

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Barone, C., D. Imparato, S. Pagano, L. Vertuccio, A. Sorrentino, and H. C. Neitzert. "Electrical Noise Characterization of Epoxy/MWCNT Composites." In Lecture Notes in Electrical Engineering. Springer Netherlands, 2011. http://dx.doi.org/10.1007/978-94-007-1324-6_6.

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Speidel, Joachim. "Characterization of the Noise at the Receiver." In Signals and Communication Technology. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00548-1_3.

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Fleming, Marvin F. "Characterization of Noise in Austenitic Stainless Steel." In Review of Progress in Quantitative Nondestructive Evaluation. Springer US, 1985. http://dx.doi.org/10.1007/978-1-4615-9421-5_26.

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Hauk, V., P. Höller, R. Oudelhoven, and W. A. Theiner. "Determination of Shot Peened Surface States Using the Magnetic Barkhausen Noise Method." In Nondestructive Characterization of Materials. Springer Berlin Heidelberg, 1989. http://dx.doi.org/10.1007/978-3-642-84003-6_56.

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Martin-Martinez, J., R. Rodriguez, and M. Nafria. "Advanced Characterization and Analysis of Random Telegraph Noise in CMOS Devices." In Noise in Nanoscale Semiconductor Devices. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-37500-3_14.

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de Luca, Luigi, Michele Girfoglio, Matteo Chiatto, and Gennaro Coppola. "Characterization of Synthetic Jet Resonant Cavities." In Flinovia - Flow Induced Noise and Vibration Issues and Aspects. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-09713-8_6.

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Conference papers on the topic "Noise characterization"

1

Staroň, Patrik, Robert Macků, Petr Sedlák, et al. "Noise Characterization of Graphene Sensors." In 2024 37th International Vacuum Nanoelectronics Conference (IVNC). IEEE, 2024. http://dx.doi.org/10.1109/ivnc63480.2024.10652510.

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Fritchie, J. W., T. Weber, J. Balajthy, M. Sweany, and A. Di Fulvio. "Characterization of High-Temperature SiPM Noise." In 2024 IEEE Nuclear Science Symposium (NSS), Medical Imaging Conference (MIC) and Room Temperature Semiconductor Detector Conference (RTSD). IEEE, 2024. http://dx.doi.org/10.1109/nss/mic/rtsd57108.2024.10655747.

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BOERSMA, HF, and RS ETIENNE. "CHARACTERIZATION OF THE NATURAL AMBIENT SOUND ENVIRONMENT." In Inter-Noise 1996. Institute of Acoustics, 2024. http://dx.doi.org/10.25144/19462.

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Sporkmann, Th, and I. Wolff. "Threeport Noise Characterization." In 21st European Microwave Conference, 1991. IEEE, 1991. http://dx.doi.org/10.1109/euma.1991.336364.

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KLEINER, M., and P. SVENSSON. "CHARACTERIZATION OF SCATTERING USING NEAR-FIELD ACOUSTIC HOLOGRAPHY." In Inter-Noise 1996. Institute of Acoustics, 2024. http://dx.doi.org/10.25144/19613.

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WEBER, R. "CHARACTERIZATION OF COMPLEX TRANSPORTATION NOISE WITH PSYCHOACOUSTICAL PARAMETERS." In Inter-Noise 1996. Institute of Acoustics, 2024. http://dx.doi.org/10.25144/19754.

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Hohls, F., N. Maire, B. Kaestner, et al. "Noise characterization of a single parameter quantized charge pump." In NOISE AND FLUCTUATIONS: 20th International Conference on Noice and Fluctuations (ICNF-2009). AIP, 2009. http://dx.doi.org/10.1063/1.3140501.

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He, Liang, Lei Du, Yi-Qi Zhuang, Jun-Lin Bao, Massimo Macucci, and Giovanni Basso. "Multi-parameters Characterization of Electromigration Noise in Metal Interconnection." In NOISE AND FLUCTUATIONS: 20th International Conference on Noice and Fluctuations (ICNF-2009). AIP, 2009. http://dx.doi.org/10.1063/1.3140555.

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Fischer, Peter, Manuel Pürscher, Severin Huemer-Kals, and Jurij Prezelj. "Characterization of Brake Creep Groan Vibrations." In 11th International Styrian Noise, Vibration & Harshness Congress: The European Automotive Noise Conference. SAE International, 2020. http://dx.doi.org/10.4271/2020-01-1505.

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Cherng, John G., and Tim Akin. "Noise Measurements and Characterization of Automotive Dampers." In Noise & Vibration Conference & Exposition. SAE International, 1993. http://dx.doi.org/10.4271/931271.

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Reports on the topic "Noise characterization"

1

Buck, S., J. Roadman, P. Moriarty, and S. Palo. Acoustic Array Development for Wind Turbine Noise Characterization. Office of Scientific and Technical Information (OSTI), 2013. http://dx.doi.org/10.2172/1111205.

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Peck, Lindamae, Peter Styles, and Sam Toon. Characterization of Seismic Noise at Selected Non-Urban Sites. Defense Technical Information Center, 2010. http://dx.doi.org/10.21236/ada535990.

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Fustin, Drew. Phase Noise Characterization of the Main Drive Line at SLAC. Office of Scientific and Technical Information (OSTI), 2002. http://dx.doi.org/10.2172/801786.

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Siderius, Martin. High Frequency Acoustic Channel Characterization for Propagation and Ambient Noise. Defense Technical Information Center, 2008. http://dx.doi.org/10.21236/ada533072.

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Bierwagen, Gordon, Douglas J. Mills, D. Tallman, and B. Skerry. Characterization of Corrosion under Marine Coatings by Electrochemical Noise Methods. Defense Technical Information Center, 1994. http://dx.doi.org/10.21236/ada327348.

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Bickford, Justin R., Neal K. Bambha, and Wayne H. Chang. Near-room-temperature Mid-infrared Photoconductor Signal and Noise Characterization. Defense Technical Information Center, 2012. http://dx.doi.org/10.21236/ada570409.

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Multanen, Eric. Characterization of quantization noise in oversampled analog to digital converters. Portland State University Library, 2000. http://dx.doi.org/10.15760/etd.6302.

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Bickel, V. T., B. Moseley, E. Hauber, M. Shirley, J. P. Williams, and D. A. Kring. CHARACTERIZATION OF SHADOWED REGIONS AT THE LUNAR SOUTH POLE. Frontier Development Lab, 2022. http://dx.doi.org/10.56272/gqkz6227.

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This image repository contains a total of 576 non map-projected HORUS (Hyper-effective nOise Removal Unet Software) post-processed images of permanently shadowed regions (PSRs) located across the Artemis exploration zone at the lunar south pole (144 individual images).

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Nyquist, Jonthan E., and Michel Boufadel. Multifractal Characterization of Geologic Noise for Improved UXO Detection and Discrimination. Defense Technical Information Center, 2008. http://dx.doi.org/10.21236/ada495593.

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Kass, M. A., Yaoguo Li, Richard Krahenbuhl, Misac Nabighian, and Douglas Oldenburg. Enhancement of TEM Data and Noise Characterization by Principal Component Analysis. Defense Technical Information Center, 2010. http://dx.doi.org/10.21236/ada571505.

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