Noise measurement

About: Noise measurement is a research topic. Over the lifetime, 19776 publications have been published within this topic receiving 308180 citations.

A general noise de-embedding procedure for packaged two-port linear active devices

Abstract: A method based on the noise correlation technique and its applications is described. The package, which need not be reciprocal, may consist of an arbitrary interconnection of linear passive elements at thermal equilibrium. Only the terminal admittance properties of the package need be known. However, in certain special cases which lead to singular submatrices of the admittance matrix, the method is inapplicable. This situation can occur when elements such as isolators are part of the package. The necessary theoretical foundation and experimental techniques to enable workers not familiar with the field to assemble the software and laboratory setup for two-port noise de-embedding is provided. The automated noise measurement system used for data acquisition and the mathematical basis for it are described in some detail. The validity of the de-embedding approach is established with extensive experimental data obtained on three MESFETs and a pseudomorphic HEMT. >

Aeroacoustics of Flight Vehicles: Theory and Practice. Volume 1: Noise Sources

Abstract: Methodology recommended to evaluate aeroacoustic related problems is provided, and approaches to their solutions are suggested without extensive tables, nomographs, and derivations. Orientation is toward flight vehicles and emphasis is on underlying physical concepts. Theoretical, experimental, and applied aspects are covered, including the main formulations and comparisons of theory and experiment. The topics covered include: propeller and propfan noise, rotor noise, turbomachinery noise, jet noise classical theory and experiments, noise from turbulent shear flows, jet noise generated by large-scale coherent motion, airframe noise, propulsive lift noise, combustion and core noise, and sonic booms.

A Physics-Based Noise Formation Model for Extreme Low-Light Raw Denoising

Abstract: Lacking rich and realistic data, learned single image denoising algorithms generalize poorly in real raw images that not resemble the data used for training. Although the problem can be alleviated by the heteroscedastic Gaussian noise model, the noise sources caused by digital camera electronics are still largely overlooked, despite their significant effect on raw measurement, especially under extremely low-light condition. To address this issue, we present a highly accurate noise formation model based on the characteristics of CMOS photosensors, thereby enabling us to synthesize realistic samples that better match the physics of image formation process. Given the proposed noise model, we additionally propose a method to calibrate the noise parameters for available modern digital cameras, which is simple and reproducible for any new device. We systematically study the generalizability of a neural network trained with existing schemes, by introducing a new low-light denoising dataset that covers many modern digital cameras from diverse brands. Extensive empirical results collectively show that by utilizing our proposed noise formation model, a network can reach the capability as if it had been trained with rich real data, which demonstrates the effectiveness of our noise formation model.

Multidimensional Scaling Analysis for Passive Moving Target Localization With TDOA and FDOA Measurements

Abstract: A new framework for positioning a moving target is introduced by utilizing time differences of arrival (TDOA) and frequency differences of arrival (FDOA) measurements collected using an array of passive sensors. It exploits the multidimensional scaling (MDS) analysis, which has been developed for data analysis in the field such as physics, geography and biology. Particularly, we present an accurate and closed-form solution for the position and velocity of a moving target. Unlike most passive target localization methods focusing on minimizing a loss function with respect to the measurement vector, the proposed method is based on the optimization of a cost function related to the scalar product matrix in the classical MDS framework. It is robust to the large measurement noise. The bias and variance of the proposed estimator is also derived. Simulation results show that the proposed estimator achieves better performance than the spherical-interpolation (SI) method and the two-step weighted least squares (WLS) approach, and it attains the Cramer-Rao lower bound at a sufficiently high noise level before the threshold effect occurs. Moreover, for the proposed estimator the threshold effect, which is a result of the nonlinear nature of the localization problem, occurs apparently later as the measurement noise increases for a near-field target.

System and method for adaptive intelligent noise suppression

Abstract: Systems and methods for adaptive intelligent noise suppression are provided. In exemplary embodiments, a primary acoustic signal is received. A speech distortion estimate is then determined based on the primary acoustic signal. The speech distortion estimate is used to derive control signals which adjust an enhancement filter. The enhancement filter is used to generate a plurality of gain masks, which may be applied to the primary acoustic signal to generate a noise suppressed signal.