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Noise (signal processing)

About: Noise (signal processing) is a research topic. Over the lifetime, 61013 publications have been published within this topic receiving 621165 citations.


Papers
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Journal ArticleDOI
TL;DR: The results indicate that the CompCor method increases the sensitivity and selectivity of fcMRI analysis, and show a high degree of interscan reliability for many fc MRI measures.
Abstract: Resting state functional connectivity reveals intrinsic, spontaneous networks that elucidate the functional architecture of the human brain. However, valid statistical analysis used to identify such networks must address sources of noise in order to avoid possible confounds such as spurious correlations based on non-neuronal sources. We have developed a functional connectivity toolbox Conn (www.nitrc.org/projects/conn) that implements the component-based noise correction method (CompCor) strategy for physiological and other noise source reduction, additional removal of movement, and temporal covariates, temporal filtering and windowing of the residual blood oxygen level-dependent (BOLD) contrast signal, first-level estimation of multiple standard functional connectivity magnetic resonance imaging (fcMRI) measures, and second-level random-effect analysis for resting state as well as task-related data. Compared to methods that rely on global signal regression, the CompCor noise reduction method all...

3,388 citations

Book
01 Jan 2004
TL;DR: This paper establishes the possibility of stable recovery under a combination of sufficient sparsity and favorable structure of the overcomplete system and shows that similar stability is also available using the basis and the matching pursuit algorithms.
Abstract: Overcomplete representations are attracting interest in signal processing theory, particularly due to their potential to generate sparse representations of signals. However, in general, the problem of finding sparse representations must be unstable in the presence of noise. This paper establishes the possibility of stable recovery under a combination of sufficient sparsity and favorable structure of the overcomplete system. Considering an ideal underlying signal that has a sufficiently sparse representation, it is assumed that only a noisy version of it can be observed. Assuming further that the overcomplete system is incoherent, it is shown that the optimally sparse approximation to the noisy data differs from the optimally sparse decomposition of the ideal noiseless signal by at most a constant multiple of the noise level. As this optimal-sparsity method requires heavy (combinatorial) computational effort, approximation algorithms are considered. It is shown that similar stability is also available using the basis and the matching pursuit algorithms. Furthermore, it is shown that these methods result in sparse approximation of the noisy data that contains only terms also appearing in the unique sparsest representation of the ideal noiseless sparse signal.

2,365 citations

Journal ArticleDOI
TL;DR: The experimental results obtained to date indicate that this technique provides an excellent two-dimensional filtering capability that will play a key role in such problems as shape recognition and signal detection.
Abstract: In the past, spatial filtering in coherent optical systems has been limited by the inability to realize practically a general complex filter. This paper describes a technique for realizing such a filter, and gives an application of spatial filtering to the problem of detecting isolated signals in a variety of noise backgrounds. The experimental results obtained to date indicate that this technique provides an excellent two-dimensional filtering capability that will play a key role in such problems as shape recognition and signal detection.

1,810 citations

Book
08 Feb 1996
TL;DR: For practicing engineers, researchers, and advanced students in signal processing, Active Noise Control Systems: Algorithms and DSP Implementations will serve as a comprehensive, state-of-the-art text/reference on this important and rapidly changing area of signal processing.
Abstract: From the Publisher: Active noise control (ANC) is rapidly becoming the most effective way to reduce noises that can otherwise be very difficult and expensive to control ANC is achieved by introducing a canceling "anti-noise" wave through an appropriate array of secondary sources When applied accurately, ANC can provide effective solutions to noise-related problems in a broad range of areas, including manufacturing and industrial operations as well as consumer products Consequently, ANC research and development has become an important focus of both industrial applications and engineering research Active Noise Control Systems: Algorithms and DSP Implementations introduces the basic concepts of ANC with an emphasis on digital signal processing (DSP) hardware and adaptive signal processing algorithms, both of which have come into prominence within the last decade The authors emphasize the practical aspects of ANC systems by combining the principles of adaptive signal processing with both experimental results and practical implementation Applications are cited in many fields and encompass all types of noise media, including air-acoustic, hydroacoustic, vibrations, and others The specific implementation stressed is based on the TMS320 family of signal processors from Texas Instruments, which are the most widely used worldwide Coverage of theory includes concise derivations and analyses of commonly used adaptive structures and algorithms for active noise control applications, which are enhanced by the inclusion of a floppy disk featuring C and assembly programs for implementing many ANC systems Mathematical representations are employed and the source code included on the disk is in a form that is easily accessible to anyone using the book For practicing engineers, researchers, and advanced students in signal processing, Active Noise Control Systems: Algorithms and DSP Implementations will serve as a comprehensive, state-of-the-art text/reference on this important and rapidly de

1,561 citations

Journal ArticleDOI
TL;DR: This paper addresses a problem arising in a context of digital communications by exploiting an orthogonality property between "signal" and "noise" subspaces to build some quadratic form whose minimization yields the desired estimates up to a scale factor.
Abstract: This paper addresses a problem arising in a context of digital communications. A digital source is transmitted through a continuous channel (the propagation medium), and several measurements are performed at the receiver, either by means of several sensors, or by oversampling the received signal compared to the emission rate. Given only these observations, the baseband equivalents of the corresponding channels have to be recovered. An orthogonality property between "signal" and "noise" subspaces is exploited to build some quadratic form whose minimization yields the desired estimates up to a scale factor. This is in the same spirit as recent works by Tong et al. (see Proc. 25th Asilomar Conf., p.856-860, 1991) but requires fewer computations. Numerical simulations demonstrate the performance of the proposed methods in a channel identification context. >

1,557 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202238
20211,674
20202,196
20192,610
20182,361
20172,326