The Experts below are selected from a list of 12906 Experts worldwide ranked by ideXlab platform
A.s. Willsky - One of the best experts on this subject based on the ideXlab platform.
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a wavelet packet approach to Transient Signal classification
Applied and Computational Harmonic Analysis, 1995Co-Authors: R.e. Learned, A.s. WillskyAbstract:Abstract Time–frequency transforms, including wavelet and wavelet packet transforms, are generally acknowledged to be useful for studying non-stationary phenomena and, in particular, have been shown or claimed to be of value in the detection and characterization of Transient Signals. In many applications time–frequency transforms are simply employed as a visual aid to be used for Signal display. Although there have been several studies reported in the literature, there is still considerable work to be done investigating the utility of wavelet and wavelet packet time–frequency transforms for automatic Transient Signal classification. This paper contributes to this ongoing investigation through the development of a non-parametric wavelet packet feature extraction procedure which identifies features to be used for the classification of Transient Signals for which explicit Signal models are not available or appropriate. Recent literature in this area is devoted to truly ad-hoc, high-dimensional, non-parametric types of classification in which one or more time–frequency transform forms the base from which a large number of features are determined by trial and error. In contrast, the wavelet-packet-based procedure presented in this paper was formulated to systematically adapt to any data dictionary within which several classes must be distinguished. This method is aimed at focusing the information in the data set to find the smallest number of features for robust, reliable classification. The promise of our method is illustrated by performing our procedure on a set of biologically generated underwater acoustic Signals. For this example the wavelet-packet-based features obtained by our method yield excellent classification results when used as input for a neural network and a nearest neighbor rule.
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Wavelet packet based Transient Signal classification
[1992] Proceedings of the IEEE-SP International Symposium on Time-Frequency and Time-Scale Analysis, 1992Co-Authors: R.e. Learned, W.c. Karl, A.s. WillskyAbstract:Nonstationary Signals are not well suited for detection and classification by traditional Fourier methods. An alternate means of analysis needs to be used so that valuable time-frequency information is not lost. The wavelet packet transform is one such time-frequency analysis tool. The authors summarize efforts which examine the feasibility of applying the wavelet packet transform to automatic Transient Signal classification through the development of a classification algorithm for biologically generated underwater acoustic Signals in ocean noise. The formulation of a wavelet-packet based feature set specific to the classification of snapping shrimp and whale clicks is given.
Shibin Wang - One of the best experts on this subject based on the ideXlab platform.
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Transient Signal analysis based on levenberg marquardt method for fault feature extraction of rotating machines
Mechanical Systems and Signal Processing, 2015Co-Authors: Shibin Wang, Weiguo Huang, Xingwu ZhangAbstract:Abstract Localized faults in rotating machines tend to result in shocks and thus excite Transient components in vibration Signals. An iterative extraction method is proposed for Transient Signal analysis based on Transient modeling and parameter identification through Levenberg–Marquardt (LM) method, and eventually for fault feature extraction. For each iteration, a double-side asymmetric Transient model is firstly built based on parametric Morlet wavelet, and then the LM method is introduced to identify the parameters of the model. With the implementation of the iterative procedure, Transients are extracted from vibration Signals one by one, and Wigner–Ville Distribution is applied to obtain time–frequency representation with satisfactory energy concentration but without cross-term. A simulation Signal is used to test the performance of the proposed method in Transient extraction, and the comparison study shows that the proposed method outperforms ensemble empirical mode decomposition and spectral kurtosis in extracting Transient feature. Finally, the effectiveness of the proposed method is verified by the applications in Transient analysis for bearing and gear fault feature extraction.
S.p. Levitan - One of the best experts on this subject based on the ideXlab platform.
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DFT - Characterization of CMOS defects using Transient Signal analysis
Proceedings 1998 IEEE International Symposium on Defect and Fault Tolerance in VLSI Systems (Cat. No.98EX223), 1998Co-Authors: J.f. Plusquellio, D.m. Chiarulli, S.p. LevitanAbstract:We present the results of hardware experiments designed to determine the relative contribution of CMOS coupling mechanisms to off-path Signal variations caused by common types of defects. The Transient Signals measured in defect-free test structures coupled to defective test structures through internodal coupling capacitors, the power supply, the well and substrate are analyzed in the time and frequency domain to determine the characteristics of the Signal variations produced by seven types of CMOS defects. The results of these experiments are used in the development of a failure analysis technique based on the analysis of Transient Signals.
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An automated technique to identify defective CMOS devices based on linear regression analysis of Transient Signal data
Proceedings 1998 IEEE International Workshop on IDDQ Testing (Cat. No.98EX232), 1998Co-Authors: J.f. Plusquellic, D.m. Chiarulli, S.p. LevitanAbstract:Transient Signal analysis is a digital device testing method that is based on the analysis of voltage Transients at multiple test points and on I/sub DD/ switching Transients on the supply rails. We show that it is possible to identify defective devices by analyzing the Transient Signals measured at test points on paths not sensitized from the defect site. The small Signal variations generated at these test points are analyzed in both the time and frequency domain. Linear regression analysis is used to show the absence of correlation in these Signals across the outputs of bridging and open drain defective devices. A statistical method and an algorithm for identifying defective devices are presented that is based on the standard deviation of regression residuals computed over a compressed representation of these Signals.
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Identification of defective CMOS devices using correlation and regression analysis of frequency domain Transient Signal data
Proceedings International Test Conference 1997, 1997Co-Authors: J.f. Plusquellic, D.m. Chiarulli, S.p. LevitanAbstract:Transient Signal analysis is a digital device testing method that is based on the analysis of voltage Transients at multiple test points and on I/sub DD/ switching Transients on the supply rails. We show that it is possible to identify defective devices by analyzing the Transient Signals produced at test points on paths not sensitized from the defect site. The small Signal variations produced at these test points are analyzed in the frequency domain. Correlation analysis shows a high degree of correlation in these Signals across the outputs of defect-free devices. We use regression analysis to show the absence of correlation across the outputs of bridging and open drain defective devices.
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digital integrated circuit testing using Transient Signal analysis
International Test Conference, 1996Co-Authors: J.f. Plusquellic, D.m. Chiarulli, S.p. LevitanAbstract:A novel approach to testing CMOS digital circuits is presented that is based on an analysis of I/sub DD/ switching Transients on the supply rails and voltage Transients at selected test points. We present simulation and hardware experiments which show distinguishable characteristics between the Transient waveforms of defective and non-defective devices. These variations are shown to exist for CMOS open drain and bridging defects, located both on and off of a sensitized path.
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Digital IC device testing by Transient Signal analysis (TSA)
Electronics Letters, 1995Co-Authors: J.f. Plusquellic, D.m. Chiarulli, S.p. LevitanAbstract:The Letter presents a new approach to testing digital circuits that uses the variations in the Transient Signals generated within digital circuits as a defect detection method. The I/sub DD/ Transients on the supply rails and voltage Transients at selected test points are sampled over a test interval. Simulation experiments show that variations in the Transient waveforms between defective and nondefective circuits exist and that these variations are sensitive to many types of defects even when they appear on off-sensitised paths. These variations can be analysed using pattern recognition techniques, and neural processing is proposed as the means of identifying the Transient waveforms of defective devices.
Huo Zixiang - One of the best experts on this subject based on the ideXlab platform.
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Neural network model-based training algorithm for Transient Signal analysis
2009 International Conference on Mechatronics and Automation, 2009Co-Authors: Huang Weili, Huo ZixiangAbstract:The quality of electricity supply has become a major concern of electric utilities in power system. In order to acquire the improved power quality, it is required to know the sources of power quality disturbances and find ways to mitigate them. This paper presents a new approach to detect and classify power quality disturbances based on wavelet transformation and neural network. By means of wavelet analysis, the properties of time-frequency domain demonstrate that the complex wavelet transformation is an excellent tool for processing the Transient Signal of power system disturbances. The feature extraction of disturbance is explored to obtain dynamic parameters, in which the combined information can be obtained from both magnitude and argument coefficients to extract the desired band of the Transient Signal and detect the disturbance source. The improved training algorithm is utilized to complete the neural network parameters initialization, acquiring good convergence. The simulation results and analysis demonstrate that the proposed method is effective for Transient Signal feature extraction.
J. Plusquellic - One of the best experts on this subject based on the ideXlab platform.
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Power supply Transient Signal analysis for defect-oriented test
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 2003Co-Authors: J. Plusquellic, C. Patel, A. Singh, A. GattikerAbstract:Transient Signal analysis (TSA) is a testing method that is based on the analysis of a set of V/sub DD/ Transient waveforms measured simultaneously at each supply port. Defect detection is performed by applying linear regression analysis to the time or frequency domain representations of these Signals. Chip-wide process variation effects introduce Signal variations that are correlated across the individual power port measurements. In contrast, defects introduce uncorrelated local variations across these measurements that can be detected as anomalies in the cross-correlation profile derived (using regression analysis) from the power port measurements of defect-free chips. This paper focuses on the application of TSA to the detection of delay faults.
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Power supply Transient Signal analysis under real process and test hardware models
Proceedings 20th IEEE VLSI Test Symposium (VTS 2002), 2002Co-Authors: A. Singh, J. Plusquellic, A. GattikerAbstract:A device testing method called Transient Signal Analysis (TSA) is subjected to elements of a real process and testing environment in this paper. Simulation experiments are designed to determine the effects of process skew (obtained from measured parameters of a real process) on the accuracy of TSA in estimating path delays from power supply I/sub DDT/ and V/sub DDT/ waveforms. The circuit model is designed to test TSA under deep submicron process models that incorporate advanced parameters such as transistor V/sub t/ width dependencies. Modeling elements of a testing environment including the probe card are subsequently introduced as a means of evaluating the effects of tester measurement noise in an actual implementation.
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Power supply Transient Signal integration circuit
Proceedings International Test Conference 2001 (Cat. No.01CH37260), 2001Co-Authors: C. Patel, F. Muradali, J. PlusquellicAbstract:We discuss a circuit which measures and analyses power supply Transients as defined in the test technique, Transient Signal Analysis (TSA). This circuit can replace the benchtop instrumentation and offline Signal processing software used in previous work. The circuit accepts voltage Transients as analog inputs from the Device-Under-Test (DUT), performs integration and outputs an analog value to the tester. The tester compares the output value to a predetermined threshold as a means of determining the pass/fail status of the DUT. This circuit is designed to simplify the hardware requirements of TSA.
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Detecting delay faults using power supply Transient Signal analysis
Proceedings International Test Conference 2001 (Cat. No.01CH37260), 2001Co-Authors: A. Singh, C. Patel, J. Plusquellic, Shirong Liao, A. GattikerAbstract:A delay-fault testing strategy based on the analysis of power supply Transient Signals is presented. The method is an extension to a Go/No-Go device testing method called Transient Signal Analysis (TSA). TSA detects defects through the analysis of a set of power supply Transient waveforms in the time or frequency domain, e.g., Fourier phase components. A recent extension to TSA demonstrated a correlation between the V/sub DDT/ Fourier phase components and path delays in defect-free devices. The method proposed here is able to detect increases in delay due to resistive shorting and open defects using a similar technique. In particular simulation results show that a delay defective device can be distinguished from a defect-free device through an anomaly in the Fourier phase correlation profile of the device.
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Predicting device performance from pass/fail Transient Signal analysis data
Proceedings International Test Conference 2000 (IEEE Cat. No.00CH37159), 2000Co-Authors: J. Plusquellic, A. Germida, J. Hudson, E. Staroswiecki, C. PatelAbstract:Transient Signal Analysis (TSA) is a Go/No-Go device testing method that is based on the analysis of voltage Transients at multiple test points. In this paper a technique based on an extension to TSA is presented that is able to predict critical path delay using data from non-critical (predictor) path tests. A characterization phase is performed a priori in which both predictor path and critical path delays are measured from a set of defect-free devices. The characterization data is used to define the relationship between the power supply Transient Signal data and the actual delays. Once established, prediction is performed during production test by simply re-analyzing the data from the predictor path Go/No-Go TSA tests, and therefore, no speed bin testing is required. Simulations on an 8-bit multiplier are used to demonstrate a linear relationship between a range of supply rail Fourier Phase harmonics and delay under various process models. The accuracy of the prediction is evaluated statistically against the measured delays from an additional set of critical path simulations.
