Adaptive enhancement of multiple sinusoids in uncorrelated noise

Zeidler, J.R.; Satorius, E.; Chabries, Douglas M.; Wexler, H.

doi:10.1109/tassp.1978.1163074

Cited by 188 publications

(44 citation statements)

References 23 publications

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“…This type of application is named an Adaptive Line Enhancer (ALE). Later Zeidler et al (1978) analysed the steady-state behaviour of the ALE for stationary inputs consisting of multiple sinusoids buried in white noise. Treichler (1979) used an eigenvalue-eigenvector technique to quantify the convergence time and characteristics of the ALE.…”

Section: Adaptive Filtering Methodsmentioning

confidence: 99%

“…In our applications at least mean squared (LMS)-based ALE is used to remove background noise in the measurements and tested using the MLS data. Figure 4 shows the structure of a LMS-based ALE (Widrow et al 1975;Zeidler et al 1978;Treichler 1979). The unfiltered signal (raw measurement data stream) consists of the narrow-band signals of interest (positivenegative peaks) together with broad-band noise, and is the primary channel of the ALE.…”

Section: Adaptive Filtering Methodsmentioning

confidence: 99%

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Digital signal processing methods for impedance microfluidic cytometry

Sun

Berkel

Green

et al. 2008

Microfluid Nanofluid

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Impedance microfluidic cytometry is a noninvasive, label-free technology that can characterize the dielectric properties of single particles (beads/cells) at high speed. In this paper we show how digital signal processing methods are applied to the impedance signals for noise removal and signal recovery in an impedance microfluidic cytometry. Two methods are used; correlation to identify typical signals from a particle and for a noisier environment, an adaptive filter is used to remove noise. The benefits of adaptive filtering are demonstrated quantitatively from the correlation coefficient and signal-to-noise ratio. Finally, the adaptive filtering method is compared to the Savitzky-Golay filtering method.

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Section: Adaptive Filtering Methodsmentioning

confidence: 99%

Section: Adaptive Filtering Methodsmentioning

confidence: 99%

Digital signal processing methods for impedance microfluidic cytometry

Sun

Berkel

Green

et al. 2008

Microfluid Nanofluid

View full text Add to dashboard Cite

show abstract

“…This , and considerations of t r a c t a b i l i t y , lead us to treat only the "growth rate" ; i.e., we set t~(O) -O . Equations ( 3) and ( 5) y ield…”

Section: ~~~mentioning

confidence: 99%

“…In particular , a detailed analysis is carried out for single pole spectra (i. e., a narrowband signa l in white noise) . Several simplified derivations of previous results ( [2] and [3]) are included in order tha t the discussion may rema in self-contained .…”

Section: Introductionmentioning

confidence: 99%

Time Constants and Learning Curves of LMS Adaptive Filters.

Shensa¹

1978

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“…This idea is well developed by statisticians (Widrow et al, 1975) and was successfully applied in telecommunications under the name Adaptive Line Enhancer (Zeidler et al, 1978). The decomposition is based on the autocorrelation features of the signal, because the correlation time of the deterministic (i.e., periodic) part is much longer than that of the nondeterministic part.…”

Section: Introductionmentioning

confidence: 99%

Decomposition of Vibration Signals into Deterministic and Nondeterministic Components and its Capabilities of Fault Detection and Identification

Barszcz¹

2009

International Journal of Applied Mathematics and Computer Science

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The paper investigates the possibility of decomposing vibration signals into deterministic and nondeterministic parts, based on the Wold theorem. A short description of the theory of adaptive filters is presented. When an adaptive filter uses the delayed version of the input signal as the reference signal, it is possible to divide the signal into a deterministic (gear and shaft related) part and a nondeterministic (noise and rolling bearings) part. The idea of the self-adaptive filter (in the literature referred to as SANC or ALE) is presented and its most important features are discussed. The flowchart of the Matlab-based SANC algorithm is also presented. In practice, bearing fault signals are in fact nondeterministic components, due to a little jitter in their fundamental period. This phenomenon is illustrated using a simple example. The paper proposes a simulation of a signal containing deterministic and nondeterministic components. The self-adaptive filter is then appliedfirst to the simulated data. Next, the filter is applied to a real vibration signal from a wind turbine with an outer race fault. The necessity of resampling the real signal is discussed. The signal from an actual source has a more complex structure and contains a significant noise component, which requires additional demodulation of the decomposed signal. For both types of signals the proposed SANC filter shows a very good ability to decompose the signal.

show abstract

Adaptive enhancement of multiple sinusoids in uncorrelated noise

Cited by 188 publications

References 23 publications

Digital signal processing methods for impedance microfluidic cytometry

Digital signal processing methods for impedance microfluidic cytometry

Time Constants and Learning Curves of LMS Adaptive Filters.

Decomposition of Vibration Signals into Deterministic and Nondeterministic Components and its Capabilities of Fault Detection and Identification

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