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Journal articles on the topic 'Noise characterization'

Author: Grafiati

Published: 4 June 2021

Last updated: 17 June 2025

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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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2

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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4

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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7

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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8

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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9

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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11

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 (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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12

Dobrowolski, Janusz A. "Noise Analysis of Differential Multiport Networks – the Wave Approach." International Journal of Electronics and Telecommunications 60, no. 4 (2014): 281–86. http://dx.doi.org/10.2478/eletel-2014-0036.

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Abstract In this paper there is presented and discussed a general analysis method for noise characterization of noisy 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 noise analysis of differential microwave multiport networks with differential as well as with conventional single ended ports.

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13

CHEN, CHIH-HUNG, and M. JAMAL DEEN. "RF CMOS NOISE CHARACTERIZATION AND MODELING." International Journal of High Speed Electronics and Systems 11, no. 04 (2001): 1085–157. http://dx.doi.org/10.1142/s0129156401001064.

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This paper presents a through description of radio frequency (RF) noise characterization and modeling of CMOS transistors. It begins with the definition of the four noise parameter of a two-port network - minimum noise figure (NFmin), equivalent noise resistance (Rn), optimized source impedance (Ropt) and optimized source reactance (Xopt). These four parameters are used in device characterization and it is shown how they can be calculated by using the noise two-port network theory and a circuit simulator. Then two de-embedding procedures are discussed in detail for noise and scattering parameter de-embedding to get rid of the parasitic effects from the probe pads and interconnections in the device-under-test (DUT). Ideally there is no frequency and geometry limitation for the method based on a cascade configuration. Methods to directly extract the channel noise, induced gate noise and their correlation from the RF and noise measurements are developed and the extracted noise sources as a function of frequency and bias condition for different channel lengths a presented. Some design consideration for the design of low noise circuits - how to select the device size, choice of DC bias conditions and design device layout, are presented. Finally, some published noise models for the channel noise, induced gate noise and their correlation are discussed.

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14

Charalambous, Charalambos D., Christos Kourtellaris, and Stelios Louka. "New Formulas of Feedback Capacity for AGN Channels with Memory: A Time-Domain Sufficient Statistic Approach." Entropy 27, no. 2 (2025): 207. https://doi.org/10.3390/e27020207.

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Recently, several papers identified technical issues related to equivalent time-domain and frequency-domain “characterization of the n–block or transmission” feedback capacity formula and its asymptotic limit, the feedback capacity, of additive Gaussian noise (AGN) channels, first introduce by Cover and Pombra in 1989 (IEEE Transactions on Information Theory). The main objective of this paper is to derive new results on the Cover and Pombra characterization of the n–block feedback capacity formula, and to clarify the main points of confusion regarding the time-domain results that appeared in the literature. The first part of this paper derives new equivalent time-domain sequential characterizations of feedback capacity of AGN channels driven by non-stationary and non-ergodic Gaussian noise. It is shown that the optimal channel input processes of the new equivalent sequential characterizations are expressed as functionals of a sufficient statistic and a Gaussian orthogonal innovations process. Further, the Cover and Pombra n–block capacity formula is expressed as a functional of two generalized matrix difference Riccati equations (DREs) of the filtering theory of Gaussian systems, contrary to results that appeared in the literature and involve only one DRE. It is clarified that prior literature deals with a simpler problem that presupposes the state of the noise is known to the encoder and the decoder. In the second part of this paper, the existence of the asymptotic limit of the n–block feedback capacity formula is shown to be equivalent to the convergence properties of solutions of the two generalized DREs. Further, necessary and or sufficient conditions are identified for the existence of asymptotic limits, for stable and unstable Gaussian noise, when the optimal input distributions are asymptotically time-invariant but not necessarily stationary. This paper contains an in-depth analysis, with various examples, and identifies the technical conditions on the feedback code and state space noise realization, so that the time-domain capacity formulas that appeared in the literature, for AGN channels with stationary noises, are indeed correct.

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15

F., Setiawan, Alexander V. Gramolin, Elisha S. Matekole, Hari Krovi, and Jacob M. Taylor. "Accurate and Honest Approximation of Correlated Qubit Noise." Quantum 9 (April 9, 2025): 1701. https://doi.org/10.22331/q-2025-04-09-1701.

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Accurate modeling of noise in realistic quantum processors is critical for constructing fault-tolerant quantum computers. While a full simulation of actual noisy quantum circuits provides information about correlated noise among all qubits and is therefore accurate, it is, however, computationally expensive as it requires resources that grow exponentially with the number of qubits. We propose an efficient systematic construction of approximate noise channels, where their accuracy can be enhanced by incorporating noise components with higher qubit-qubit correlation degree. To formulate such approximate channels, we first present a method, dubbed the cluster expansion approach, to decompose the Lindbladian generator of an actual noise channel into components based on interqubit correlation degree. We generate a k-th order approximate noise channel by truncating the cluster expansion and incorporating noise components with correlations up to the k-th degree. We require that the approximate noise channels must be accurate and also “honest", i.e., the actual errors are not underestimated in our physical models. As an example application, we apply our method to model noise in a three-qubit quantum processor that stabilizes a [[2,0,2]] codeword, which is one of the four Bell states. We find that, for realistic noise strength typical for fixed-frequency superconducting qubits coupled via always-on static interactions, correlated noise beyond two-qubit correlation can significantly affect the code simulation accuracy. Since our approach provides a systematic characterization of multi-qubit noise correlations, it enables the potential for accurate, honest and scalable approximations to simulate large numbers of qubits from full modeling or experimental characterizations of small enough quantum subsystems, which are efficient yet still retain essential noise features of the entire device.

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Song, Junyang, Bo Lu, Lu Liu, and Chuan Wang. "Noisy Quantum Channel Characterization Using Quantum Neural Networks." Electronics 12, no. 11 (2023): 2430. http://dx.doi.org/10.3390/electronics12112430.

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Channel noise is considered to be the main obstacle in long-distance quantum communication and distributed quantum networks. Here, employing a quantum neural network, we present an efficient method to study the model and detect the noise of quantum channels. Based on various types of noisy quantum channel models, we construct the architecture of the quantum neural network and the model training process. Finally, we perform experiments to verify the training effectiveness of the scheme, and the results show that the cost function of the quantum neural network could approach above 90% of the channel model.

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INDECK, R. S., M. N. JOHNSON, Guo MIAN, J. R. HOINVILLE, and M. W. MULLER. "NOISE CHARACTERIZATION OF PERPENDICULAR MEDIA." Journal of the Magnetics Society of Japan 15, S_2_PMRC_91 (1991): S2_173–178. http://dx.doi.org/10.3379/jmsjmag.15.s2_173.

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18

Wales, Stephen C. "Ship‐radiated noise‐field characterization." Journal of the Acoustical Society of America 101, no. 5 (1997): 3197. http://dx.doi.org/10.1121/1.419342.

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19

Randa, J. "Noise characterization of multiport amplifiers." IEEE Transactions on Microwave Theory and Techniques 49, no. 10 (2001): 1757–63. http://dx.doi.org/10.1109/22.954781.

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20

O’Brien, Kevin P., and M. B. Weissman. "Statistical characterization of Barkhausen noise." Physical Review E 50, no. 5 (1994): 3446–52. http://dx.doi.org/10.1103/physreve.50.3446.

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21

Chung, Dong Myung, Un Cig Ji, and Nobuaki Obata. "Higher Powers of Quantum White Noises in Terms of Integral Kernel Operators." Infinite Dimensional Analysis, Quantum Probability and Related Topics 01, no. 04 (1998): 533–59. http://dx.doi.org/10.1142/s0219025798000296.

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A rigorous mathematical formulation of higher powers of quantum white noises is given on the basis of the most recent theory of white noise distributions due to Cochran, Kuo and Sengupta. The renormalized quantum Itô formula due to Accardi, Lu and Volovich is derived from the renormalized product formula based on integral kernel operators on white noise functions. During the discussion, the analytic characterization of operator symbols and the expansion theorem for a white noise operator in terms of integral kernel operators are established.

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22

Gonçalves, Luana M., Gabriel A. e. S. Ferraz, Marcelo S. de Oliveira, Brenon D. S. Barbosa, Carlos J. da Silva, and Patrícia F. P. Ferraz. "Characterization of noise emitted by a power tiller through geostatistics." Revista Brasileira de Engenharia Agrícola e Ambiental 23, no. 3 (2019): 223–28. http://dx.doi.org/10.1590/1807-1929/agriambi.v23n3p223-228.

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ABSTRACT Noise is one of the harmful and stressful physical agents present in the workplace. Research performed with geostatistics to adjust the semivariogram of tractor noise were performed using the Gaussian and spherical model. In this way, the aim was to map the spatial variability of the noise emitted by a power tiller through the sine wave model, besides testing other fitting methods, in order to identify health zones for the workers. The experiment was performed with an agricultural power tiller (10.3 kW) placed in a working regime (1500 rpm), coupled to a brushcutter, and a digital sound level meter to collect noises in points distributed along a regular grid sampling of 2.0 × 2.0 m around the tractor. The spatial dependence of noise was analyzed through semivariogram fitting by different methods and by the wave model to obtain the spatial distribution map interpolated by kriging. It was possible to characterize the structure and the magnitude of the spatial dependence of the noise levels emitted by the tractor, as well as to map the spatial distribution. A maximum noise level of 96.5 dB was observed close to the tractor engine, a value above the limit of 85.0 dB for 8 h of daily exposure (NR-15). Thus, the use of ear protectors is recommended for both the tractor driver and the professionals who support agricultural operation within a radius of 6 m from the emitting source under the conditions studied.

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CHEN, C. H., Y. L. WANG, and M. H. BAKR. "WAVE-BASED APPROACH FOR MICROWAVE NOISE CHARACTERIZATION." Fluctuation and Noise Letters 08, no. 01 (2008): R1—R14. http://dx.doi.org/10.1142/s0219477508004313.

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The noise behavior of a two-port is usually described through the conventional set of noise parameters Fmin, Rn, and the complex Yopt. However, noise parameters developed using wave-based techniques also have their merit as they could offer different insights to a two-port's noise behavior. Unlike the conventional noise parameters, these wave-based noise parameters could be terminal-invariant and describe only the intrinsic noise behavior of a two-port. In this paper, several important noise parameters derived from wave-based approaches are reviewed. The derivation of each set of parameters is discussed and illustrated. The measurement approach of each set of parameters is also briefly covered.

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Oyedepo, J. A., D. M. Omoniyi, D. E. Oluyege, and E. I. Babajide. "Spatial characterization of noise levels at the Federal University of Agriculture, Abeokuta, Nigeria." Nigerian Journal of Environmental Sciences and Technology 4, no. 2 (2020): 323–40. http://dx.doi.org/10.36263/nijest.2020.02.0218.

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The study investigated the spatial variability in the distribution of noise pollution in Federal University of Agriculture, Abeokuta. Measurements of noise were taken from 10 locations on campus namely; the academic environment including the University library and college lecture auditoria, the residential areas comprising the Student hostels and Vice-chancellor’s lodge as well as other populated areas like the car park and student union building. The noise measurement was done in the morning and evenings of Mondays, Wednesdays and Saturdays over a period of 3 weeks in July 2019. The noise measurement was done with the aid of Smart Sensor Digital Sound Level meter (Model: AR824). The sampling points were geo-located using a hand-held receiver for Global Positioning Systems (GPS). Questionnaires were administered to members of the University community (staff and students) to determine their respective perception of campus noise on academic activities. Data (noise and survey) were subjected to statistical analysis. Spatial analysis of the noise levels includes surface interpolation (Krigging) to determine the spatial pattern of noise across the campus, particularly, the most tranquil and most chaotic locations. The results show noise pollution levels reaching 74.3 (db) and 73.0 (db) during weekdays at some locations on campus. The noise emission level at some locations within the University exceeded the World Health Organization and Federal Ministry of Environment of Nigeria’s permissible level of 55 db for residential areas. Generally, it was observed that the ambient noise from heavy duty generating with the student chattering put noise level above 35(db) to 55(db) recommended for educational institutions. It can be inferred from statistical analyses and spatial interpolations of recorded noise levels, that noise levels of many areas in FUNAAB exceed the recommended 40 db required for an institution of higher learning. The study however finds that staff and students have adjusted to the noise on campus. Lecturers have resorted to the use of public address systems in large classes while students go to serene locations for better assimilation when studying. The study recommends reduction of point-source noises such as replacing the diesel generators with solar power. Signage should be displayed at sensitive areas such as library to reduce unnecessary noise.

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Doi, Eizaburo, and Michael S. Lewicki. "Characterization of Minimum Error Linear Coding with Sensory and Neural Noise." Neural Computation 23, no. 10 (2011): 2498–510. http://dx.doi.org/10.1162/neco_a_00181.

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Robust coding has been proposed as a solution to the problem of minimizing decoding error in the presence of neural noise. Many real-world problems, however, have degradation in the input signal, not just in neural representations. This generalized problem is more relevant to biological sensory coding where internal noise arises from limited neural precision and external noise from distortion of sensory signal such as blurring and phototransduction noise. In this note, we show that the optimal linear encoder for this problem can be decomposed exactly into two serial processes that can be optimized separately. One is Wiener filtering, which optimally compensates for input degradation. The other is robust coding, which best uses the available representational capacity for signal transmission with a noisy population of linear neurons. We also present spectral analysis of the decomposition that characterizes how the reconstruction error is minimized under different input signal spectra, types and amounts of degradation, degrees of neural precision, and neural population sizes.

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Bhuvnesh Kumar Sharma. "Exponential Characteristics of Room Impulse Response system in Noisy Environment." Journal of Electrical Systems 20, no. 3 (2024): 2146–60. http://dx.doi.org/10.52783/jes.4012.

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This paper describes the characteristics of the Room Impulse Response system for an exponential signal in presence of different noises. Traditionally Room impulse response function used for acoustic applications. Characterization of this system helps to develop its new applications. The room impulse response system is characterized using frequency domain analysis. To obtain the phase and magnitude responses, the spectrum for an exponential signal is computed and then convolved with a room impulse response system. Further the signal is exposed to the noise and then convolve it to the system and get the output for different signal to noise ratios. This whole simulation executes in MATLAB. It is discovered that the Room Impulse Response system exhibits a low pass filter magnitude response and a peace-wise linear phase response when an exponential signal is applied in the presence of different noises. For varying noise levels, more parallels in the Room Impulse Response system's phase and magnitude output were discovered. This technique characterizes Room Impulse Response for exponential signals in noisy environment. A model to implement Room Impulse Response function as system also provided. Here Room Impulse Response function used as system and it is completely characterized for exponential signal in AWGN, Exponential, Rayleigh, Poisson noise. This work opens up entirely new uses for the Room Impulse Response system, like low pass and band pass filtering.

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Czerwinski, Artur. "Entanglement characterization by single-photon counting with random noise." Quantum Information and Computation 22, no. 1&2 (2022): 1–16. http://dx.doi.org/10.26421/qic22.1-2-1.

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In this article, we investigate the problem of entanglement characterization by polarization measurements combined with maximum likelihood estimation (MLE). A realistic scenario is considered with measurement results distorted by random experimental errors. In particular, by imposing unitary rotations acting on the measurement operators, we can test the performance of the tomographic technique versus the amount of noise. Then, dark counts are introduced to explore the efficiency of the framework in a multi-dimensional noise scenario. The concurrence is used as a figure of merit to quantify how well entanglement is preserved through noisy measurements. Quantum fidelity is computed to quantify the accuracy of state reconstruction. The results of numerical simulations are depicted on graphs and discussed.

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He, Sheng-Wu, Jia-Gang Wang, and Rong-Qin Yao. "The characterizations of Laplacians in white noise analysis." Nagoya Mathematical Journal 143 (September 1996): 93–109. http://dx.doi.org/10.1017/s0027763000005936.

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The Laplacians form a class of the most important differential operators in white noise analysis. The goal of this paper is to give their characterizations. Our main tools are the Fock expansions of operators in terms of integral kernel operators and rotation-invariance. In Section 1, the fundamental setting of white noise analysis is introduced briefly. In Section 2, integral kernel operators and the Fock expansions of operators are given. The characterization theorems for number operator, Gross-Laplacian and Euler operator are given in Sections 3, 4 and 5 respectively.

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Chapeau-Blondeau, François. "Simulating Quantum Pauli Noise with Three Independently Controlled Pauli Gates." Electronics 13, no. 2 (2024): 439. http://dx.doi.org/10.3390/electronics13020439.

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A quantum Pauli noise is a nonunitary process that alters the state of a qubit by random application of the four Pauli operators. We investigate a four-qubit quantum circuit, consisting of a pipeline of three independently controlled Pauli gates, for simulating the general class of qubit Pauli noises. The circuit with a fixed architecture is controllable by three separable quantum states from three auxiliary qubits in order to adjust the parameters of the targeted Pauli noise on the principal qubit. Important Pauli noises such as bit flip, phase flip, bit phase flip, and depolarizing noise are readily simulated, along with an infinite subset of other Pauli noises. However, the quantum circuit with its simple and fixed architecture cannot simulate all conceivable Pauli noises, and a characterization is proposed, in the parameter space of the Pauli noises, denoting those that are simulable by the circuit and those that are not. The circuit is a useful tool to contribute to controlled simulation, on current or future quantum processors, of nonunitary processes of noise and decoherence.

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30

Navid, Reza, Thomas H. Lee, and Robert W. Dutton. "Circuit-Based Characterization of Device Noise Using Phase Noise Data." IEEE Transactions on Circuits and Systems I: Regular Papers 57, no. 6 (2010): 1265–72. http://dx.doi.org/10.1109/tcsi.2009.2033535.

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31

Campbell, Steven, Alan Wall, Corey Taylor, Frank Mobley, and Reese Rasband. "Large-scale anechoic characterization of small caliber firearm impulse noise." INTER-NOISE and NOISE-CON Congress and Conference Proceedings 264, no. 1 (2022): 111–20. http://dx.doi.org/10.3397/nc-2022-702.

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Three-dimensional source characterization is crucial for determining noise metrics produced by operating a firearm in indoor environments. Anechoic measurements for sound power and directivity measurement are challenging for small caliber firearms. In order to create high fidelity source models of an M4A1 firearm (firing blanks), a team from Ball Aerospace, Inc., the Air Force Research Laboratory, and Brigham Young University conducted an expansive characterization within an anechoic facility located at the Owens Corning Acoustic Research Complex using a two-meter spherical microphone array centered on the muzzle of the weapon. The array consisted of a quarter sphere of over forty microphones at various elevation and azimuth angles. By rotating the weapon, an entire closed sphere of over one hundred points were sampled with multiple shots. Differences in directivity, sound power level and shot to shot repeatability are examined for two weapon configurations. These differences reinforce the idea that accurate source characterizations are required for modelling small caliber firearm noise propagation. The views expressed are those of the author and do not necessarily reflect the official policy or position of the Department of the Air Force, the Department of Defense, or the U.S. government.

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32

LARNDER, CHRIS, NICOLAS DESAULNIERS-SOUCY, SHAUN LOVEJOY, DANIEL SCHERTZER, CLAUDE BRAUN, and D. LAVALLEE. "UNIVERSAL MULTIFRACTAL CHARACTERIZATION AND SIMULATION OF SPEECH." International Journal of Bifurcation and Chaos 02, no. 03 (1992): 715–19. http://dx.doi.org/10.1142/s0218127492000835.

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In the 1970's it was found that; for low frequencies (<10 Hz), speech is scaling: it has no characteristic time scale. Now such scale invariance is associated with multiscaling statistics, and multifractal structures. Just as Gaussian noises frequently arise because they are generically produced by sums of many independent noise processes, scaling noises have an analogous universal behavior arising from nonlinear mixing of processes. We show that low frequency speech is consistent with these ideas, and use the measured parameters to produce stochastic speech simulations which are strikingly similar to real speech.

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33

Chiang, Chen-Fu, and Chang-Yu Hsieh. "Noise Characterization: Keeping Reduction Based Per-turbed Quantum Walk Search Optimal." EPJ Web of Conferences 198 (2019): 00001. http://dx.doi.org/10.1051/epjconf/201919800001.

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In a recent work by Novo et al. (Sci. Rep. 5, 13304, 2015), the invariant subspace method was applied to the study of continuous-time quantum walk (CTQW). In this work, we adopt the aforementioned method to investigate the optimality of a perturbed quantum walk search of a marked element in a noisy environment on various graphs. We formulate the necessary condition of the noise distribution in the system such that the invariant subspace method remains effective and eﬃcient. Based on the noise, we further formulate how to set the appropriate coupling factor to preserve the optimality of the quantum walker.

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34

Tahon, Marie, Silvio Montresor, and Pascal Picart. "Towards Reduced CNNs for De-Noising Phase Images Corrupted with Speckle Noise." Photonics 8, no. 7 (2021): 255. http://dx.doi.org/10.3390/photonics8070255.

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Digital holography is a very efficient technique for 3D imaging and the characterization of changes at the surfaces of objects. However, during the process of holographic interferometry, the reconstructed phase images suffer from speckle noise. In this paper, de-noising is addressed with phase images corrupted with speckle noise. To do so, DnCNN residual networks with different depths were built and trained with various holographic noisy phase data. The possibility of using a network pre-trained on natural images with Gaussian noise is also investigated. All models are evaluated in terms of phase error with HOLODEEP benchmark data and with three unseen images corresponding to different experimental conditions. The best results are obtained using a network with only four convolutional blocks and trained with a wide range of noisy phase patterns.

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35

Ephraim, Gutmark, Cuppoletti Dan, Mora Pablo, and Heeb Nicholas. "IL04 CHARACTERIZATION OF SUPERSONIC JET NOISE PRODUCTION AND METHODS FOR ITS SUPPRESSION." Proceedings of the International Conference on Jets, Wakes and Separated Flows (ICJWSF) 2013.4 (2013): _IL04–1_—_IL04–11_. http://dx.doi.org/10.1299/jsmeicjwsf.2013.4._il04-1_.

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36

Rubiolo, Pablo R., and Michael Y. Young. "ICONE15-10711 CHARACTERIZATION OF UNSTEADY CORE FLOW PATTERNS THROUGH NOISE ANALYSIS TECHNIQUES." Proceedings of the International Conference on Nuclear Engineering (ICONE) 2007.15 (2007): _ICONE1510. http://dx.doi.org/10.1299/jsmeicone.2007.15._icone1510_382.

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37

DiNitto, Julie M., and John M. Kenney. "Noise Characterization in Circular Dichroism Spectroscopy." Applied Spectroscopy 66, no. 2 (2012): 180–87. http://dx.doi.org/10.1366/11-06417.

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38

Keith, Reginald H. "Characterization of noise sources for predictions." Journal of the Acoustical Society of America 92, no. 4 (1992): 2451. http://dx.doi.org/10.1121/1.404550.

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39

Freundorfer, A. P., J. Y. Siddiqui, Y. M. M. Antar, and T. Thayaparan. "Characterization of Ultrawideband Antennas Using Noise." IEEE Antennas and Wireless Propagation Letters 9 (2010): 1263–66. http://dx.doi.org/10.1109/lawp.2011.2105237.

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40

Li, Sing-Rong, William McMahon, Yin-Lung R. Lu, and Yung-Huei Lee. "RTS Noise Characterization in Flash Cells." IEEE Electron Device Letters 29, no. 1 (2008): 106–8. http://dx.doi.org/10.1109/led.2007.910776.

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41

Heine, John J., and Madhusmita Behera. "Aspects of signal-dependent noise characterization." Journal of the Optical Society of America A 23, no. 4 (2006): 806. http://dx.doi.org/10.1364/josaa.23.000806.

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42

Veitch, James G., and Allan R. Wilks. "A characterization of Arctic undersea noise." Journal of the Acoustical Society of America 77, no. 3 (1985): 989–99. http://dx.doi.org/10.1121/1.392067.

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43

Chen, Rui, and Henrik Schmidt. "Characterization of arctic ambient noise environment." Journal of the Acoustical Society of America 141, no. 5 (2017): 3532. http://dx.doi.org/10.1121/1.4987457.

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44

Accadia, T., F. Acernese, F. Antonucci, et al. "Tools for noise characterization in Virgo." Journal of Physics: Conference Series 243 (August 1, 2010): 012004. http://dx.doi.org/10.1088/1742-6596/243/1/012004.

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45

Gennaretti, M., J. Serafini, G. Bernardini, A. Castorrini, G. De Matteis, and G. Avanzini. "Numerical characterization of helicopter noise hemispheres." Aerospace Science and Technology 52 (May 2016): 18–28. http://dx.doi.org/10.1016/j.ast.2016.02.013.

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46

Dasgupta, Samudra, and Travis S. Humble. "Characterizing the Reproducibility of Noisy Quantum Circuits." Entropy 24, no. 2 (2022): 244. http://dx.doi.org/10.3390/e24020244.

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The ability of a quantum computer to reproduce or replicate the results of a quantum circuit is a key concern for verifying and validating applications of quantum computing. Statistical variations in circuit outcomes that arise from ill-characterized fluctuations in device noise may lead to computational errors and irreproducible results. While device characterization offers a direct assessment of noise, an outstanding concern is how such metrics bound the reproducibility of a given quantum circuit. Here, we first directly assess the reproducibility of a noisy quantum circuit, in terms of the Hellinger distance between the computational results, and then we show that device characterization offers an analytic bound on the observed variability. We validate the method using an ensemble of single qubit test circuits, executed on a superconducting transmon processor with well-characterized readout and gate error rates. The resulting description for circuit reproducibility, in terms of a composite device parameter, is confirmed to define an upper bound on the observed Hellinger distance, across the variable test circuits. This predictive correlation between circuit outcomes and device characterization offers an efficient method for assessing the reproducibility of noisy quantum circuits.

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47

Maffei, Luigi, Massimiliano Masullo, Chiara Bartalucci, Arnaldo Melloni, and Raffaella Bellomini. "Pockets of quiet characterization in the historical center of Florence (Italy)." Noise Mapping 9, no. 1 (2022): 249–68. http://dx.doi.org/10.1515/noise-2022-0162.

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Abstract Due to the difficulty of accessing quiet urban areas as defined by the EU Directive 2002/49/EC and of designing new ones, especially in the historical centers, it becomes more and more important to recognize pockets of quiet which can provide visitors with time for their physical and mental restoration. To this aim, an investigation methodology has been developed in previous studies conducted in Naples (Italy), Istanbul (Turkey) and Murcia (Spain) focused on finding and characterization of alternative quiet spaces which could offer opportunities to rest from the surrounding noise, which highlighted the role of non-auditory aspects on the restorativeness of urban spaces, such as green and water elements, or of historic and cultural value. This methodology which includes objective and subjective assessment has been used to characterize and illustrate the potentialities of the existing and potential spaces for restoration within the ancient center of the city of Florence (Italy). Concerning acoustic measurements, the “quietness” is confirmed by the fact that the differences between the sound levels (LAeq) of the surrounding areas and those within selected sites is greater than 9 dB; while the outcomes of the survey confirm the expectations especially concerning the importance and relevance, the cultural value, the historical-artistic elements, the perceived sense of being away and the distinction with the external acoustic environment.

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48

Gupta, M. S., and P. T. Greiling. "Microwave noise characterization of GaAs MESFET's: determination of extrinsic noise parameters." IEEE Transactions on Microwave Theory and Techniques 36, no. 4 (1988): 745–51. http://dx.doi.org/10.1109/22.3580.

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49

Falferi, P., R. Mezzena, and A. Vinante. "Full noise characterization of a low-noise two-stage SQUID amplifier." Superconductor Science and Technology 22, no. 7 (2009): 075020. http://dx.doi.org/10.1088/0953-2048/22/7/075020.

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50

Djennati, Z. A., and K. Ghaffour. "Noise Characterization in InAlAs/InGaAs/InP pHEMTs for Low Noise Applications." International Journal of Electrical and Computer Engineering (IJECE) 7, no. 1 (2017): 176. http://dx.doi.org/10.11591/ijece.v7i1.pp176-183.

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In this paper, a noise revision of an InAlAs/InGaAs/InP psoeudomorphic high electron mobility transistor (pHEMT) in presented. The noise performances of the device were predicted over a range of frequencies from 1GHz to 100GHz. The minimum noise figure (NFmin), the noise resistance (Rn) and optimum source impedance (Zopt) were extracted using two approaches. A physical model that includes diffusion noise and G-R noise models and an analytical model based on an improved PRC noise model that considers the feedback capacitance Cgd. The two approaches presented matched results allowing a good prediction of the noise behaviour. The pHEMT was used to design a single stage S-band low noise amplifier (LNA). The LNA demonstrated a gain of 12.6dB with a return loss coefficient of 2.6dB at the input and greater than -7dB in the output and an overall noise figure less than 1dB.

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