Nonlinear System Identification Using a Subband Adaptive Volterra Filter

Burton, Trevor; Goubran, Rafik; Beaucoup, Franck

doi:10.1109/tim.2009.2012939

Cited by 50 publications

(15 citation statements)

References 14 publications

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“…The mean fault recognition rate of the method in Ref. [18] is 93.3%, which is good enough in practical fault diagnosis. However, it is not good at diagnosis of the U1 and U2 faults, and its worst fault recognition rate is 79.5% (in the U2 fault), less than that of the proposed method.…”

Section: Simulationmentioning

confidence: 81%

“…From Table 3 we can observe that the proposed method has a better recognition capability than that of the method in Ref. [18], which also uses the Volterra series to diagnosis analog circuits. The mean fault recognition rate of the proposed method is 97.5%, while its worst fault recognition rate is 92.5% (in the U1 fault), which are both better than the other method.…”

Section: Simulationmentioning

confidence: 94%

“…But the soft fault features are too small to identify, the Volterra model in Ref. [18] should be improved further.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Diagnosis of soft faults in analog integrated circuits based on fractional correlation

Deng¹,

Shi²,

Zhang³

2012

J. Semicond.

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Aiming at the problem of diagnosing soft faults in analog integrated circuits, an approach based on fractional correlation is proposed. First, the Volterra series of the circuit under test (CUT) decomposed by the fractional wavelet packet are used to calculate the fractional correlation functions. Then, the calculated fractional correlation functions are used to form the fault signatures of the CUT. By comparing the fault signatures, the different soft faulty conditions of the CUT are identified and the faults are located. Simulations of benchmark circuits illustrate the proposed method and validate its effectiveness in diagnosing soft faults in analog integrated circuits.

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Section: Simulationmentioning

confidence: 81%

Section: Simulationmentioning

confidence: 94%

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Diagnosis of soft faults in analog integrated circuits based on fractional correlation

Deng¹,

Shi²,

Zhang³

2012

J. Semicond.

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“…In this paper, we present a nonlinear clipping detector to identify nonnegligible nonlinear distortion periods using a portion of the second-order Volterra filter [9][10][11], which efficiently characterizes speaker distortion [12,13]. Thus, the nonlinear clipping detector pauses the linear adaptive filter activity during the nonlinear clipping period so that the linear filter is updated only for the linear echo signal, which is the first approach of detecting nonlinear clipping periods without a priori clipping information.…”

Section: Introductionmentioning

confidence: 99%

A second-order Volterra filter-based nonlinear clipping detector

Park¹,

Chang²

2016

Turk J Elec Eng & Comp Sci

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Abstract:In this paper, we propose a novel nonlinear clipping detector based on the second-order Volterra filter for an acoustic echo canceller (AEC). Since the performance of the conventional AEC algorithm drastically deteriorates when nonlinear clipping occurs, the adaptive filter pauses adaptation during the nonlinear clipping periods to avoid unwanted divergence. These nonlinear clipping periods are detected in our method using the magnitude spectrum of the quadratic Volterra filter in lower frequency ranges. Through extensive computer-based simulation results considering an acoustic room environment and various clipping levels, it is found that the proposed approach is effective in detecting the nonlinear clipping periods and improving AEC performance compared to the conventional AEC algorithm.

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“…Compared with the full-band adaptive filtering technique, several studies [157] [158] have proved that the sub-band adaptive filtering technique has better performance, especially when the impulse response is long [159] [160]. The basic structure of the normalized sub-band adaptive filter (NSAF) system [161] is shown in Figure 9.1.…”

Section: Future Workmentioning

confidence: 99%

Signal Enhancement Applied to Pulse Transit Time Measurement

He¹

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High arterial blood pressure, or hypertension, is a major risk factor for cardiovascular diseases. Conventional noninvasive methods for estimating the arterial blood pressure rely on a brachial cuff that is placed around the upper arm. The cuff is inflated to a pressure that exceeds the systolic blood pressure. The heart pulse is monitored while the cuff is slowly deflated. The pressure at which the heart pulse sound can be detected corresponds to the systolic pressure and the pressure at which the heart pulse sound can no longer be detected corresponds to the diastolic pressure. This approach cannot be used on a continuous basis and has many disadvantages, including the fact that the system is cumbersome and causes discomfort to the patient, which may affect their blood pressure value. This thesis proposes two approaches to accurately measure the pulse transit time (PTT). The PTT can be used to estimate arterial blood pressure variations noninvasively, on a continuous basis without the use of a cuff. The correlation between the arterial blood pressure and PTT are verified using bio-signals from the MIMIC II database. The first approach for measuring the PTT relies on the electrocardiogram (ECG) and photo-plethysmograph (PPG) signals. Algorithms based on the empirical mode decomposition (EMD) method and the adaptive filtering techniques are proposed to enhance the corrupted ECG signal. Gaussian-based curve fitting algorithms are proposed to model and extract the embedded characteristic parameters from the original PPG signal.

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Nonlinear System Identification Using a Subband Adaptive Volterra Filter

Cited by 50 publications

References 14 publications

Diagnosis of soft faults in analog integrated circuits based on fractional correlation

Diagnosis of soft faults in analog integrated circuits based on fractional correlation

A second-order Volterra filter-based nonlinear clipping detector

Signal Enhancement Applied to Pulse Transit Time Measurement

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