Network analysis by digital computer

Kuo, Franklin F.

doi:10.1109/proc.1966.4887

Cited by 49 publications

(15 citation statements)

References 28 publications

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“…Additionally, given a fixed amount of temporal observations, the trade-off between the length of a data segment and the number of the segments is essential for the bias and the variance in spectral estimation (Oppenheim, et al 1999). A Kaiser window (Kuo and Kaiser 1966) may be used to tune the necessary frequency resolution. The selection of temporal segments can be proceeded using the “1/2 overlap” principle, which estimates the instantaneous covariance from data length l by overlapping l /2 samples from previous segments.…”

Section: Discussionmentioning

confidence: 99%

Dynamic Granger–Geweke causality modeling with application to interictal spike propagation

Lin

Hara

Solo

et al. 2009

Human Brain Mapping

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A persistent problem in developing plausible neurophysiological models of perception, cognition, and action is the difficulty of characterizing the interactions between different neural systems. Previous studies have approached this problem by estimating causal influences across brain areas activated during cognitive processing using Structural Equation Modeling and, more recently, with Granger-Geweke causality. While SEM is complicated by the need for a priori directional connectivity information, the temporal resolution of dynamic Granger-Geweke estimates is limited because the underlying autoregressive (AR) models assume stationarity over the period of analysis. We have developed a novel optimal method for obtaining data-driven directional causality estimates with high temporal resolution in both time and frequency domains. This is achieved by simultaneously optimizing the length of the analysis window and the chosen AR model order using the SURE criterion. Dynamic Granger-Geweke causality in time and frequency domains is subsequently calculated within a moving analysis window. We tested our algorithm by calculating the Granger-Geweke causality of epileptic spike propagation from the right frontal lobe to the left frontal lobe. The results quantitatively suggested the epileptic activity at the left frontal lobe was propagated from the right frontal lobe, in agreement with the clinical diagnosis. Our novel computational tool can be used to help elucidate complex directional interactions in the human brain.

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Section: Discussionmentioning

confidence: 99%

Dynamic Granger–Geweke causality modeling with application to interictal spike propagation

Lin

Hara

Solo

et al. 2009

Human Brain Mapping

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“…When using the analogy between electrical and mechanical transmission lines [8] it is known that the transmission matrix of two transmission lines connected in tandem is equal to the product of the individual matrices [9]. This can be generalized to obtain the expression of the transmission matrix for any number of transmission lines connected in tandem.…”

Section: Discrete Time Modelmentioning

confidence: 99%

Discrete Time Model for Bond Detection in Thin Layer Materials

2012

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Recursive equations were developed and introduced that enable to calculate the transmitted waves through a multiplicity of layers. The calculations are carried out using z-transform technique. The layers are considered lossless and have parallel interface, which are at normal incidence to the eld. The multilayered structures are constructed of three aluminum plates connected together with epoxy as the adhesive layers. The method has been successfully used to detect small changes in the thickness of adhesive layers.

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“…Differentiators are used extensively in many practical systems [1][2][3], and consequently their design is of considerable interest. Nonrecursive digital differentiators satisfying prescribed specifications can be designed by using the equiripple approach advanced by Rabiner, McClellan, and Parks [4][5][6] or by using the Fourier-series method in conjunction with the Kaiser window function [7][8][9][10]. In the first approach, the passband error is minimised using the Remez exchange algorithm, so as to achieve an equiripple weighted error.…”

Section: Indexing Terms: Digital Circuits Digital Signal Processingmentioning

confidence: 99%

“…Digital differentiators can be easily designed by using the Fourier series method for the design of nonrecursive digital filters in conjunction with the Kaiser window function [7][8][9][10]. The transfer function of this class of differentiators is given by…”

Section: Differentiators Based On the Kaiser Window Functionmentioning

confidence: 99%

Improved design method for kaiser differentiators and comparison with equiripple method

Antoniou

Charalambous

1981

IEE Proc. E Comput. Digit. Tech. UK

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The paper describes several improvements in a method reported recently by Antoniou for the design of digital differentiators satisfying prescribed specifications. The improvements include the use of a more efficient optimisation algorithm, and a simple and accurate technique for the prediction of the required differentiator order. The improved design method is illustrated by several examples, and is compared with the equiripple design method. List of symbols E(a, N, co) = error function £>ei> e 2,£3 = specific values of L(a,N) F(a),fi(a) = nonlinear functions of a H(z) = discrete-time transfer function h(nT) = impulse response 7 0 (x) = zeroth-order Bessel function of the first kind k = stage of minimisation L(pc,N) = maximum ofE(ot,N, co) over the passband m = number of functions //(a) M(a, N, co) = amplitude response of digital differentiator MJ(OJ) = amplitude response of ideal differentiator N = number of samples in impluse response ,-Wi, N 2 , N 3 = specific values of Af T = sampling period, s w(nT) = window function a = parameter of Kaiser window function a* = global minimum point a 0 , a fe , a, a = specific values of a 8 = prescribed maximum inband error e, €j = minimisation tolerances Co» ^10 = specific values of e and e x Tj= change in a for function //a) T max = maximum of 77 co = frequency variable, rad/s co p = passband edge, rad/s co fi = sampling frequency, rad/s

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Network analysis by digital computer

Cited by 49 publications

References 28 publications

Dynamic Granger–Geweke causality modeling with application to interictal spike propagation

Dynamic Granger–Geweke causality modeling with application to interictal spike propagation

Discrete Time Model for Bond Detection in Thin Layer Materials

Improved design method for kaiser differentiators and comparison with equiripple method

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