Measuring Volterra kernels of analog to digital converters using a stepped three-tone scan

Bjorsell,; Ronnow,; Handel,

doi:10.1109/imtc.2006.328342

Cited by 5 publications

(8 citation statements)

References 3 publications

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“…In the case of common polynomials, the output y(n) can be expressed in the following form H y(n) Z aix'(n) (2) i=O where ai are the coefficients of the nonlinearity up to the maximum order Hm, which is the highest harmonic component, too.…”

Section: A Common Polynomialsmentioning

confidence: 99%

“…Chebyshev polynomials of the first kind T,(x) are defined as 7T (cos(x)) = cos(nx) (7) where n is the order of the polynomial. A recursive formula using two previous polynomials can be used for the calculation (8) Fourier coefficients can also be used to compose the output signal in a similar manner to (2). The equation describing the output signal is then CHo + y(n) = + Ch cos(hnT) 2 h=l (9) where Ch are Fourier coefficients representing the output and XI is set to 1 (normalized).…”

Section: B Chebyshev Polynomialsmentioning

confidence: 99%

“…It behaves not only nonlinearly but it also shows memory effects [2]. Further on, the nonlinearity depends on the input test signal frequency and amplitude.…”

Section: Introductionmentioning

confidence: 99%

“…In the contributions [2,4,5,7,10] the approximation of the curve of the INL(n) is taken by different approaches. The INL(n) can be divided into two components, low codefrequency (LcFINL(n)) and high code-frequency (HcFINL(n)) [6,11].…”

Section: Introductionmentioning

confidence: 99%

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Models of the ADC transfer function¿sensitivity to noise

Haasz

Slepička

Suchanek

2008

2008 IEEE Instrumentation and Measurement Technology Conference

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Section: A Common Polynomialsmentioning

confidence: 99%

Section: B Chebyshev Polynomialsmentioning

confidence: 99%

“…It behaves not only nonlinearly but it also shows memory effects [2]. Further on, the nonlinearity depends on the input test signal frequency and amplitude.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Models of the ADC transfer function¿sensitivity to noise

Haasz

Slepička

Suchanek

2008

2008 IEEE Instrumentation and Measurement Technology Conference

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show abstract

“…In [8] it is shown that by measuring Volterra kernels and comparing the frequency response with the properties of a given model structure, an appropriate model can be chosen. For the considered ADC, the Volterra kernels have the symmetry of a parallel Hammerstein system [8]. The parallel Hammerstein model is an extension of the ordinary Hammerstein model.…”

Section: Introductionmentioning

confidence: 99%

Post-Correction of Under-Sampled Analog to Digital Converters

Björsell

Händel

2007

2007 IEEE Instrumentation &Amp; Measurement Technology Conference IMTC 2007

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Applications with wide bandwidth and high center frequencies force the analog to digital converter (ADC) to be active in a working range with less dynamic performance in relation to lower frequency bands. However using under-sampling techniques in combination with post-correction methods enable a combination of high sampling rate, wide bandwidth and low distortion. In this paper the employed dynamic post-correction is a combination of look-up tables and model based correction. The results are mainly based on measurements on a 12-bit 210 MSPS ADC. The improvement in total harmonic distortion and spurious free dynamic range are acceptable over a wide frequency range and it is robust to variations in amplitude.

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Nonlinear structural damage detection using output‐only Volterra series model

Peng

Hao

et al. 2021

Structural Contr & Hlth

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Summary Volterra series is a promising technique with great potential for nonlinear system identification. The conventional Volterra series model computes the output responses by performing multiple convolutions between the input excitation and Volterra kernels function. However, the difficulty in acquiring the excitation forces of civil engineering structures under operating conditions greatly limits the application of using Volterra series‐based method for system identification. This paper proposes an output‐only‐based approach using Volterra series model for nonlinear structural damage detection, by quantifying the nonlinear behavior of structures without the prior knowledge of external excitations. The proposed approach uses the structural responses measured at two different locations to identify the kernel function parameters and evaluate the contribution of nonlinear components in the measured responses. The ratio between the standard deviation of the nonlinear components and that of the overall structural response is adopted as damage‐sensitive index to quantify the contributions from these two adjacent sensors for performing nonlinear structural damage detection. Numerical studies on a beam structure with a breathing crack under different levels of white noise excitations and experimental studies on a precast segmental concrete column subjected to ground motions with different peak ground acceleration (PGA) values are conducted to validate the capability and accuracy of using the proposed approach for nonlinear structural damage detection. The results demonstrate that the proposed approach is capable of performing nonlinearity quantification effectively and locating structural nonlinear damage. The increasing damage index value can also be used to register the increasing damage severity.

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Measuring Volterra kernels of analog to digital converters using a stepped three-tone scan

Cited by 5 publications

References 3 publications

Models of the ADC transfer function¿sensitivity to noise

Models of the ADC transfer function¿sensitivity to noise

Post-Correction of Under-Sampled Analog to Digital Converters

Nonlinear structural damage detection using output‐only Volterra series model

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